{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceType":"datasetVersion","sourceId":16258019,"datasetId":10422925,"databundleVersionId":17241713}],"dockerImageVersionId":31328,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:33.268174Z","iopub.execute_input":"2026-05-16T06:27:33.268938Z","iopub.status.idle":"2026-05-16T06:27:33.288784Z","shell.execute_reply.started":"2026-05-16T06:27:33.268895Z","shell.execute_reply":"2026-05-16T06:27:33.287822Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/datasets/tasdidhasnain/multi-cloud-master-dataset/MULTICLOUD_MASTER_DATASET.csv\n","output_type":"stream"}],"execution_count":83},{"cell_type":"code","source":"import os, json, time, warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, TensorDataset\n \nfrom sklearn.model_selection import train_test_split\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import (classification_report, confusion_matrix,\n    accuracy_score, f1_score, precision_score, recall_score)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:33.290335Z","iopub.execute_input":"2026-05-16T06:27:33.290711Z","iopub.status.idle":"2026-05-16T06:27:33.295828Z","shell.execute_reply.started":"2026-05-16T06:27:33.290687Z","shell.execute_reply":"2026-05-16T06:27:33.294879Z"}},"outputs":[],"execution_count":84},{"cell_type":"code","source":"# ── Force CPU if CUDA still fails ─────────────────────────────────\ntry:\n    if torch.cuda.is_available():\n        # Test CUDA works before committing to it\n        test = torch.tensor([1.0]).cuda()\n        _ = test * 2\n        DEVICE = torch.device('cuda')\n        print(f\"✅ GPU: {torch.cuda.get_device_name(0)}\")\n    else:\n        DEVICE = torch.device('cpu')\n        print(\"✅ Using CPU\")\nexcept Exception as e:\n    print(f\"⚠ CUDA failed ({e}) → falling back to CPU\")\n    DEVICE = torch.device('cpu')\n\nprint(f\"   Device: {DEVICE}\")\nprint(f\"   PyTorch: {torch.__version__}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:33.296945Z","iopub.execute_input":"2026-05-16T06:27:33.297241Z","iopub.status.idle":"2026-05-16T06:27:33.308845Z","shell.execute_reply.started":"2026-05-16T06:27:33.297210Z","shell.execute_reply":"2026-05-16T06:27:33.308067Z"}},"outputs":[{"name":"stdout","text":"✅ GPU: Tesla T4\n   Device: cuda\n   PyTorch: 2.10.0+cu128\n","output_type":"stream"}],"execution_count":85},{"cell_type":"code","source":"# ── Load Master Dataset ─────────────────────────────────────\nDATASET_PATH = None\nfor root, dirs, files in os.walk('/kaggle/input/'):   # ← generic path\n    for f in files:\n        if f.endswith('.csv'):\n            DATASET_PATH = os.path.join(root, f)\n            break\n\nprint(f\"📂 Loading: {DATASET_PATH}\")\ndf = pd.read_csv(DATASET_PATH, low_memory=False)\nprint(f\"✅ Loaded: {len(df):,} records × {len(df.columns)} columns\")\n\n# Save cloud_platform NOW before df gets overwritten later\ncloud_platform_values = df['cloud_platform'].values   # ← ADD THIS\nprint(f\"   Platform: {pd.Series(cloud_platform_values).value_counts().to_dict()}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:33.310643Z","iopub.execute_input":"2026-05-16T06:27:33.311011Z","iopub.status.idle":"2026-05-16T06:27:36.486668Z","shell.execute_reply.started":"2026-05-16T06:27:33.310988Z","shell.execute_reply":"2026-05-16T06:27:36.485908Z"}},"outputs":[{"name":"stdout","text":"📂 Loading: /kaggle/input/datasets/tasdidhasnain/multi-cloud-master-dataset/MULTICLOUD_MASTER_DATASET.csv\n✅ Loaded: 248,828 records × 29 columns\n   Platform: {'Azure': 146595, 'AWS': 102233}\n","output_type":"stream"}],"execution_count":86},{"cell_type":"code","source":"# ── CELL 3: Handle Missing Values ──────────────────────────────────\nprint(\"\\n=== STEP 1: MISSING VALUES ===\")\nfill_map = {\n    'actor_ip': '0.0.0.0', 'actor_identity': 'unknown',\n    'status_code': 'UNKNOWN', 'subscription_id': 'N/A',\n    'resource_group': 'N/A', 'attack_type': 'None',\n    'request_id': 'N/A', 'disclosure_status': 'N/A',\n}\nfor col, val in fill_map.items():\n    if col in df.columns:\n        df[col] = df[col].fillna(val)\nprint(f\"  Nulls remaining: {df.isnull().sum().sum()} ✅\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:36.488163Z","iopub.execute_input":"2026-05-16T06:27:36.488559Z","iopub.status.idle":"2026-05-16T06:27:36.956439Z","shell.execute_reply.started":"2026-05-16T06:27:36.488495Z","shell.execute_reply":"2026-05-16T06:27:36.955704Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 1: MISSING VALUES ===\n  Nulls remaining: 0 ✅\n","output_type":"stream"}],"execution_count":87},{"cell_type":"code","source":"# ── CELL 4: Temporal Features ──────────────────────────────────────\nprint(\"\\n=== STEP 2: TEMPORAL FEATURES ===\")\nnorm = pd.DataFrame()\ndf['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce', utc=True)\nnorm['hour']        = df['timestamp'].dt.hour.fillna(0).astype(int)\nnorm['minute']      = df['timestamp'].dt.minute.fillna(0).astype(int)\nnorm['second']      = df['timestamp'].dt.second.fillna(0).astype(int)\nnorm['day_of_week'] = df['timestamp'].dt.dayofweek.fillna(0).astype(int)\nnorm['is_weekend']  = (norm['day_of_week'] >= 5).astype(int)\nnorm['is_night']    = ((norm['hour'] < 6) | (norm['hour'] >= 22)).astype(int)\nnorm['is_business'] = ((norm['hour'] >= 9) & (norm['hour'] < 17) & (norm['is_weekend'] == 0)).astype(int)\nnorm['time_sin']    = np.sin(2 * np.pi * norm['hour'] / 24)\nnorm['time_cos']    = np.cos(2 * np.pi * norm['hour'] / 24)\nprint(\"  9 features extracted ✅\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:36.957641Z","iopub.execute_input":"2026-05-16T06:27:36.957966Z","iopub.status.idle":"2026-05-16T06:27:37.317840Z","shell.execute_reply.started":"2026-05-16T06:27:36.957939Z","shell.execute_reply":"2026-05-16T06:27:37.317091Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 2: TEMPORAL FEATURES ===\n  9 features extracted ✅\n","output_type":"stream"}],"execution_count":88},{"cell_type":"code","source":"# ── CELL 5: Network Features ───────────────────────────────────────\nprint(\"\\n=== STEP 3: NETWORK FEATURES ===\")\n \ndef extract_ip(ip_series):\n    parts = ip_series.astype(str).str.split('.', expand=True)\\\n            .apply(pd.to_numeric, errors='coerce').fillna(0).astype(int)\n    parts.columns = range(len(parts.columns))\n    out = pd.DataFrame()\n    out['ip_oct1']     = parts.get(0, 0)\n    out['ip_oct2']     = parts.get(1, 0)\n    out['ip_oct3']     = parts.get(2, 0)\n    out['ip_oct4']     = parts.get(3, 0)\n    out['is_private']  = ip_series.apply(lambda x: 1 if str(x).startswith(\n        ('10.','172.16','172.17','172.18','172.19','172.2','172.3','192.168.')) else 0)\n    out['is_loopback'] = (parts.get(0, 0) == 127).astype(int)\n    out['ip_class']    = pd.cut(parts.get(0,0), bins=[0,127,191,223,239,255],\n                                labels=[0,1,2,3,4], include_lowest=True).astype(int)\n    return out\n \nip_feats = extract_ip(df['actor_ip'])\nfor c in ip_feats.columns:\n    norm[c] = ip_feats[c].values\n \ndef parse_ports(rid):\n    try:\n        if '->' in str(rid):\n            src, dst = str(rid).split('->')\n            sp = int(src.rsplit(':',1)[1]) if ':' in src else 0\n            dp = int(dst.rsplit(':',1)[1]) if ':' in dst else 0\n            return max(0,min(sp,65535)), max(0,min(dp,65535))\n    except: pass\n    return 0, 0\n \nports            = df['resource_id'].apply(parse_ports)\nnorm['src_port'] = [p[0] for p in ports]\nnorm['dst_port'] = [p[1] for p in ports]\nnorm['port_log'] = np.log1p(norm['dst_port'])\n \nnorm['is_well_known_port'] = (norm['dst_port'] < 1024).astype(int)\nnorm['is_registered_port'] = ((norm['dst_port'] >= 1024) & (norm['dst_port'] < 49152)).astype(int)\nnorm['is_ephemeral_port']  = (norm['dst_port'] >= 49152).astype(int)\nnorm['is_ssh_port']        = (norm['dst_port'] == 22).astype(int)\nnorm['is_http_port']       = (norm['dst_port'] == 80).astype(int)\nnorm['is_https_port']      = (norm['dst_port'] == 443).astype(int)\nnorm['is_web_port']        = norm['dst_port'].isin([80,443,8080,8443]).astype(int)\nnorm['is_dns_port']        = (norm['dst_port'] == 53).astype(int)\nnorm['is_db_port']         = norm['dst_port'].isin([3306,5432,1433,6379,27017]).astype(int)\nnorm['is_mail_port']       = norm['dst_port'].isin([25,110,143,465,587,993,995]).astype(int)\nnorm['is_remote_port']     = norm['dst_port'].isin([22,23,3389,5900]).astype(int)\nnorm['is_ntp_port']        = (norm['dst_port'] == 123).astype(int)\nprint(\"  22 features extracted ✅\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:37.319774Z","iopub.execute_input":"2026-05-16T06:27:37.320014Z","iopub.status.idle":"2026-05-16T06:27:39.592907Z","shell.execute_reply.started":"2026-05-16T06:27:37.319992Z","shell.execute_reply":"2026-05-16T06:27:39.591973Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 3: NETWORK FEATURES ===\n  22 features extracted ✅\n","output_type":"stream"}],"execution_count":89},{"cell_type":"code","source":"# ── CELL 6: Categorical Encoding ───────────────────────────────────\nprint(\"\\n=== STEP 4: CATEGORICAL ENCODING ===\")\n \nstatus_map = {\n    'Blocked':0,'TCP-Deny':0,'UDP-Deny':0,\n    'Allowed':1,'Accept':1,'Success':1,'Resolved':1,\n    'TCP-Begin':2,'TCP-Continue':2,'TCP-End':2,\n    'Start':3,'Active':3,'Updated':3,'ContainerNotFound':4,\n}\nnorm['result_status_unified'] = df['result_status'].map(status_map).fillna(5).astype(int)\nnorm['is_blocked'] = df['result_status'].isin(['Blocked','TCP-Deny','UDP-Deny']).astype(int)\nnorm['is_allowed'] = df['result_status'].isin(['Allowed','Accept','Success']).astype(int)\n \ncode_map = {\n    'NX':0,'ACCEPT':1,'REJECT':2,\n    'DefaultRule_DenyAllInBound':3,\n    'DefaultRule_AllowInternetOutBound':4,\n    'UserRule_SSH':5,'PlatformRule':6,\n}\nnorm['status_code_enc'] = df['status_code'].map(code_map).fillna(7).astype(int)\n \nle_logcat                = LabelEncoder()\nnorm['log_category_enc'] = le_logcat.fit_transform(df['log_category'].fillna('Unknown'))\n \nle_method                    = LabelEncoder()\nnorm['collection_method_enc'] = le_method.fit_transform(df['collection_method'].fillna('Unknown'))\n \nnorm['is_aws']   = (df['cloud_platform'] == 'AWS').astype(int)\nnorm['is_azure'] = (df['cloud_platform'] == 'Azure').astype(int)\n \ndef parse_event(et):\n    et = str(et)\n    return {\n        'evt_inbound': int('Inbound' in et),   'evt_outbound': int('Outbound' in et),\n        'evt_deny':    int('Deny' in et or 'REJECT' in et),\n        'evt_accept':  int('ACCEPT' in et),    'evt_dos':    int('DoS'  in et),\n        'evt_ddos':    int('DDoS' in et),      'evt_scan':   int('Scan' in et or 'PortScan' in et),\n        'evt_ssh':     int('SSH'  in et),      'evt_bg':     int('Background' in et),\n        'evt_vnet':    int('VNet' in et),      'evt_vpc':    int('VPC'  in et),\n    }\nevt_feats = df['event_type'].apply(parse_event).apply(pd.Series)\nfor c in evt_feats.columns:\n    norm[c] = evt_feats[c].values\n \nle_evt                 = LabelEncoder()\nnorm['event_type_enc'] = le_evt.fit_transform(df['event_type'].fillna('Unknown'))\n \nnorm['legal_hold_enc']          = df['legal_hold'].astype(int)\nle_legal                        = LabelEncoder()\nnorm['legal_basis_enc']         = le_legal.fit_transform(df['legal_basis'].fillna('Unknown'))\nle_appr                         = LabelEncoder()\nnorm['approval_status_enc']     = le_appr.fit_transform(df['approval_status'].fillna('Unknown'))\nle_cls                          = LabelEncoder()\nnorm['data_classification_enc'] = le_cls.fit_transform(df['data_classification'].fillna('Unknown'))\n \nprint(\"  20 features encoded ✅\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:27:39.594125Z","iopub.execute_input":"2026-05-16T06:27:39.594481Z","iopub.status.idle":"2026-05-16T06:28:25.139428Z","shell.execute_reply.started":"2026-05-16T06:27:39.594455Z","shell.execute_reply":"2026-05-16T06:28:25.138670Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 4: CATEGORICAL ENCODING ===\n  20 features encoded ✅\n","output_type":"stream"}],"execution_count":90},{"cell_type":"code","source":"# ── CELL 7: Label (label_multiclass ONLY) ──────────────────────────\nprint(\"\\n=== STEP 5: LABEL → label_multiclass ONLY ===\")\n \nattack_map = {\n    'DoS-SYNFlood':'DoS',      'DDoS-SSH-Flood':'DDoS',\n    'DDoS-HTTP-Flood':'DDoS',  'DDoS-HTTPS-Flood':'DDoS',\n    'DDoS-MultiVector':'DDoS', 'DDoS-Other':'DDoS',\n    'PortScan':'PortScan',     'PortScan-Background':'PortScan',\n    'SSH-BruteForce':'SSH',    'SSH-Scan':'SSH',\n    'Background-Scan':'Normal','Background-Noise':'Normal',\n    'None':'Normal',\n}\ndf['attack_cat']         = df['attack_type'].map(attack_map).fillna('Normal')\nle_multi                 = LabelEncoder()\nnorm['label_multiclass'] = le_multi.fit_transform(df['attack_cat'])\n \nprint(f\"\\n  Class mapping:\")\nfor cls, idx in zip(le_multi.classes_, le_multi.transform(le_multi.classes_)):\n    cnt = (norm['label_multiclass'] == idx).sum()\n    print(f\"    {idx} = {cls:10s}  →  {cnt:>8,} records ({cnt/len(norm)*100:.1f}%)\")\nprint(\"\\n✅ label_multiclass created\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:25.140770Z","iopub.execute_input":"2026-05-16T06:28:25.141185Z","iopub.status.idle":"2026-05-16T06:28:25.224784Z","shell.execute_reply.started":"2026-05-16T06:28:25.141157Z","shell.execute_reply":"2026-05-16T06:28:25.224080Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 5: LABEL → label_multiclass ONLY ===\n\n  Class mapping:\n    0 = DDoS        →   102,071 records (41.0%)\n    1 = DoS         →    85,347 records (34.3%)\n    2 = Normal      →    18,574 records (7.5%)\n    3 = PortScan    →    25,405 records (10.2%)\n    4 = SSH         →    17,431 records (7.0%)\n\n✅ label_multiclass created\n","output_type":"stream"}],"execution_count":91},{"cell_type":"code","source":"# ── CELL 8: Scale Continuous Features ─────────────────────────────\nprint(\"\\n=== STEP 6: STANDARD SCALING ===\")\n \ncontinuous_cols = [\n    'hour','minute','second','src_port','dst_port',\n    'ip_oct1','ip_oct2','ip_oct3','ip_oct4',\n    'port_log','time_sin','time_cos'\n]\nscaler                = StandardScaler()\nnorm[continuous_cols] = scaler.fit_transform(norm[continuous_cols])\nprint(f\"  {len(continuous_cols)} continuous features scaled to μ=0, σ=1 ✅\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:25.225845Z","iopub.execute_input":"2026-05-16T06:28:25.226162Z","iopub.status.idle":"2026-05-16T06:28:25.292451Z","shell.execute_reply.started":"2026-05-16T06:28:25.226129Z","shell.execute_reply":"2026-05-16T06:28:25.291651Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 6: STANDARD SCALING ===\n  12 continuous features scaled to μ=0, σ=1 ✅\n","output_type":"stream"}],"execution_count":92},{"cell_type":"code","source":"# ── Validate & Save ────────────────────────────────────────\nprint(\"\\n=== STEP 7: VALIDATE & SAVE ===\")\n\nfeature_cols = [c for c in norm.columns if c != 'label_multiclass']\nnulls = norm.isnull().sum().sum()\ninfs  = np.isinf(norm[feature_cols].select_dtypes(include=[np.number])).sum().sum()\n\nprint(f\"  Records  : {len(norm):,}\")\nprint(f\"  Features : {len(feature_cols)}\")\nprint(f\"  Nulls    : {nulls}\")\nprint(f\"  Infs     : {infs}\")\n\n# ── ADD THIS BEFORE SAVING ─────────────────────────────────\nnorm['cloud_platform'] = cloud_platform_values   # ← ADD THIS LINE\n\nOUTPUT = '/kaggle/working/NORMALIZED_MULTICLOUD_DATASET.csv'\nnorm.to_csv(OUTPUT, index=False)\nprint(f\"\\n✅ Saved → {OUTPUT}\")\nprint(f\"   Size   : {os.path.getsize(OUTPUT)/1024/1024:.1f} MB\")\nprint(f\"\\n  cloud_platform check:\")\nprint(norm['cloud_platform'].value_counts().to_string())   # ← verify","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:25.293666Z","iopub.execute_input":"2026-05-16T06:28:25.294028Z","iopub.status.idle":"2026-05-16T06:28:32.444648Z","shell.execute_reply.started":"2026-05-16T06:28:25.294004Z","shell.execute_reply":"2026-05-16T06:28:32.443402Z"}},"outputs":[{"name":"stdout","text":"\n=== STEP 7: VALIDATE & SAVE ===\n  Records  : 248,828\n  Features : 55\n  Nulls    : 0\n  Infs     : 0\n\n✅ Saved → /kaggle/working/NORMALIZED_MULTICLOUD_DATASET.csv\n   Size   : 78.5 MB\n\n  cloud_platform check:\ncloud_platform\nAzure    146595\nAWS      102233\n","output_type":"stream"}],"execution_count":93},{"cell_type":"code","source":"# ── CELL 10: Summary ───────────────────────────────────────────────\nprint(f\"\"\"\n{'='*55}\n  NORMALIZATION COMPLETE ✅\n{'='*55}\n  File    : NORMALIZED_MULTICLOUD_DATASET.csv\n  Records : {len(norm):,}\n  Features: {len(feature_cols)}\n  Label   : label_multiclass\n \n  Classes:\n    0 = DDoS      {(norm['label_multiclass']==0).sum():>8,} records\n    1 = DoS       {(norm['label_multiclass']==1).sum():>8,} records\n    2 = Normal    {(norm['label_multiclass']==2).sum():>8,} records\n    3 = PortScan  {(norm['label_multiclass']==3).sum():>8,} records\n    4 = SSH       {(norm['label_multiclass']==4).sum():>8,} records\n \n  Feature breakdown (55 total):\n    Temporal      :  9\n    Network (IP)  : 10\n    Port flags    : 12\n    Status        :  4\n    Categorical   :  5\n    Event flags   : 11\n    Governance    :  4\n\n{'='*55}\n\"\"\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:32.445875Z","iopub.execute_input":"2026-05-16T06:28:32.446220Z","iopub.status.idle":"2026-05-16T06:28:32.455483Z","shell.execute_reply.started":"2026-05-16T06:28:32.446192Z","shell.execute_reply":"2026-05-16T06:28:32.454633Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  NORMALIZATION COMPLETE ✅\n=======================================================\n  File    : NORMALIZED_MULTICLOUD_DATASET.csv\n  Records : 248,828\n  Features: 55\n  Label   : label_multiclass\n \n  Classes:\n    0 = DDoS       102,071 records\n    1 = DoS         85,347 records\n    2 = Normal      18,574 records\n    3 = PortScan    25,405 records\n    4 = SSH         17,431 records\n \n  Feature breakdown (55 total):\n    Temporal      :  9\n    Network (IP)  : 10\n    Port flags    : 12\n    Status        :  4\n    Categorical   :  5\n    Event flags   : 11\n    Governance    :  4\n\n=======================================================\n\n","output_type":"stream"}],"execution_count":94},{"cell_type":"code","source":"DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f\"✅ Libraries loaded\")\nprint(f\"   Device: {DEVICE}\")\nif DEVICE.type == 'cuda':\n    print(f\"   GPU: {torch.cuda.get_device_name(0)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:32.458915Z","iopub.execute_input":"2026-05-16T06:28:32.459266Z","iopub.status.idle":"2026-05-16T06:28:32.469966Z","shell.execute_reply.started":"2026-05-16T06:28:32.459241Z","shell.execute_reply":"2026-05-16T06:28:32.469082Z"}},"outputs":[{"name":"stdout","text":"✅ Libraries loaded\n   Device: cuda\n   GPU: Tesla T4\n","output_type":"stream"}],"execution_count":95},{"cell_type":"code","source":"# ── CELL 11: Load Normalised Dataset ───────────────────────────────────────────\nDATASET_PATH = None\nfor root, dirs, files in os.walk('/kaggle/working/'):\n    for f in files:\n        if f.endswith('.csv'):\n            DATASET_PATH = os.path.join(root, f)\n            break\nif DATASET_PATH is None:\n    for f in os.listdir('.'):\n        if f.endswith('.csv'):\n            DATASET_PATH = f; break\n \nprint(f\"\\n📂 Loading: {DATASET_PATH}\")\ndf = pd.read_csv(DATASET_PATH, low_memory=False)\nprint(f\"✅ Loaded: {len(df):,} records × {len(df.columns)} columns\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:32.473961Z","iopub.execute_input":"2026-05-16T06:28:32.474790Z","iopub.status.idle":"2026-05-16T06:28:34.499368Z"}},"outputs":[{"name":"stdout","text":"\n📂 Loading: /kaggle/working/NORMALIZED_MULTICLOUD_DATASET.csv\n✅ Loaded: 248,828 records × 57 columns\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# Separate features / label / platform\nLABEL        = 'label_multiclass'\nDROP         = [LABEL, 'cloud_platform', 'label_binary', 'label_3class']\nfeature_cols = [c for c in df.columns if c not in DROP and c in df.columns]\n \nX_raw        = df[feature_cols].fillna(0).values.astype(np.float32)\ny            = df[LABEL].values\nplatform     = df['cloud_platform'].values if 'cloud_platform' in df.columns else None\n \nprint(f\"\\n  Input features : {len(feature_cols)}\")\nprint(f\"  Records        : {len(X_raw):,}\")\nprint(f\"  Classes        : {dict(sorted(Counter(y).items()))}\")\n ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:34.502966Z","iopub.execute_input":"2026-05-16T06:28:34.503262Z","iopub.status.idle":"2026-05-16T06:28:34.664442Z"}},"outputs":[{"name":"stdout","text":"\n  Input features : 55\n  Records        : 248,828\n  Classes        : {np.int64(0): 102071, np.int64(1): 85347, np.int64(2): 18574, np.int64(3): 25405, np.int64(4): 17431}\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Scale Features ─────────────────────────────────────────\nscaler  = StandardScaler()\nX_scaled = scaler.fit_transform(X_raw).astype(np.float32)\n \nINPUT_DIM      = X_scaled.shape[1]\nBOTTLENECK_DIM = 20          # ← Final feature count for your DL models\nHIDDEN_1       = 40\nHIDDEN_2       = 30\nBATCH_SIZE     = 512\nEPOCHS         = 50\nLR             = 1e-3\n \nprint(f\"\\n  Input dim      : {INPUT_DIM}\")\nprint(f\"  Bottleneck dim : {BOTTLENECK_DIM}  ← compressed features\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:34.668077Z","iopub.execute_input":"2026-05-16T06:28:34.668368Z","iopub.status.idle":"2026-05-16T06:28:34.860023Z"}},"outputs":[{"name":"stdout","text":"\n  Input dim      : 55\n  Bottleneck dim : 20  ← compressed features\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Define Autoencoder ─────────────────────────────────────\nclass ForensicAutoencoder(nn.Module):\n    def __init__(self, input_dim, bottleneck_dim, h1=40, h2=30, dropout=0.2):\n        super().__init__()\n\n        # Encoder — NO BatchNorm (causes CUDA kernel issues on some GPUs)\n        self.encoder = nn.Sequential(\n            nn.Linear(input_dim, h1),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n\n            nn.Linear(h1, h2),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n\n            nn.Linear(h2, bottleneck_dim),\n            nn.ReLU(),\n        )\n\n        # Decoder\n        self.decoder = nn.Sequential(\n            nn.Linear(bottleneck_dim, h2),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n\n            nn.Linear(h2, h1),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n\n            nn.Linear(h1, input_dim),\n        )\n\n    def encode(self, x):\n        return self.encoder(x)\n\n    def decode(self, z):\n        return self.decoder(z)\n\n    def forward(self, x):\n        z     = self.encode(x)\n        recon = self.decode(z)\n        return recon, z\n \n \nmodel     = ForensicAutoencoder(INPUT_DIM, BOTTLENECK_DIM, HIDDEN_1, HIDDEN_2).to(DEVICE)\noptimizer = torch.optim.Adam(model.parameters(), lr=LR, weight_decay=1e-5)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n \ntotal_params = sum(p.numel() for p in model.parameters())\nprint(f\"\\n  Model parameters: {total_params:,}\")\nprint(f\"\\n  Architecture:\")\nprint(f\"    Input     : {INPUT_DIM}\")\nprint(f\"    Encoder   : {INPUT_DIM} → {HIDDEN_1} → {HIDDEN_2} → {BOTTLENECK_DIM}\")\nprint(f\"    Bottleneck: {BOTTLENECK_DIM}  ← FEATURE SPACE\")\nprint(f\"    Decoder   : {BOTTLENECK_DIM} → {HIDDEN_2} → {HIDDEN_1} → {INPUT_DIM}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:34.863919Z","iopub.execute_input":"2026-05-16T06:28:34.864373Z","iopub.status.idle":"2026-05-16T06:28:34.878463Z"}},"outputs":[{"name":"stdout","text":"\n  Model parameters: 8,215\n\n  Architecture:\n    Input     : 55\n    Encoder   : 55 → 40 → 30 → 20\n    Bottleneck: 20  ← FEATURE SPACE\n    Decoder   : 20 → 30 → 40 → 55\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Train Autoencoder ──────────────────────────────────────\nprint(f\"\\n{'='*55}\")\nprint(f\"  TRAINING AUTOENCODER ({EPOCHS} epochs)\")\nprint(f\"{'='*55}\")\n\nX_tensor = torch.tensor(X_scaled, dtype=torch.float32)\nloader   = DataLoader(TensorDataset(X_tensor), batch_size=BATCH_SIZE, shuffle=True)\n\nX_tr, X_val = train_test_split(X_scaled, test_size=0.1, random_state=42)\nval_loader  = DataLoader(\n    TensorDataset(torch.tensor(X_val, dtype=torch.float32)),\n    batch_size=BATCH_SIZE\n)\n\nhistory    = {'train_loss': [], 'val_loss': []}\nbest_loss  = float('inf')\nbest_state = None\n\nfor epoch in range(EPOCHS):\n    # ── Train ──────────────────────────────────────────────────\n    model.train()\n    train_loss = 0\n    for (batch_x,) in loader:\n        batch_x      = batch_x.to(DEVICE)\n        recon, z     = model(batch_x)\n        loss         = F.mse_loss(recon, batch_x)\n        optimizer.zero_grad()\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n        optimizer.step()\n        train_loss  += loss.item()\n\n    scheduler.step()\n    avg_train = train_loss / len(loader)\n\n    # ── Validate ───────────────────────────────────────────────\n    model.eval()\n    val_loss = 0\n    with torch.no_grad():\n        for (batch_x,) in val_loader:\n            batch_x   = batch_x.to(DEVICE)\n            recon, _  = model(batch_x)\n            val_loss += F.mse_loss(recon, batch_x).item()\n    avg_val = val_loss / len(val_loader)\n\n    history['train_loss'].append(avg_train)\n    history['val_loss'].append(avg_val)\n\n    if avg_val < best_loss:\n        best_loss  = avg_val\n        best_state = {k: v.clone() for k, v in model.state_dict().items()}\n\n    if (epoch + 1) % 10 == 0 or epoch == 0:\n        print(f\"  Epoch {epoch+1:3d}/{EPOCHS}  \"\n              f\"Train={avg_train:.6f}  Val={avg_val:.6f}\")\n\nmodel.load_state_dict(best_state)\nprint(f\"\\n  ★ Best Val Loss: {best_loss:.6f}\")\n\n# ── Plot training curve ────────────────────────────────────────\nplt.figure(figsize=(10, 4))\nplt.plot(history['train_loss'], label='Train Loss', color='#e74c3c')\nplt.plot(history['val_loss'],   label='Val Loss',   color='#3498db')\nplt.xlabel('Epoch')\nplt.ylabel('MSE Loss')\nplt.title('Autoencoder Training Curve')\nplt.legend()\nplt.grid(alpha=0.3)\nplt.tight_layout()\nplt.savefig('/kaggle/working/autoencoder_training_curve.png', dpi=150)\nplt.show()\nprint(\"  📊 Training curve saved\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:28:34.879621Z","iopub.execute_input":"2026-05-16T06:28:34.879886Z","iopub.status.idle":"2026-05-16T06:31:01.571733Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  TRAINING AUTOENCODER (50 epochs)\n=======================================================\n  Epoch   1/50  Train=0.449495  Val=0.261646\n  Epoch  10/50  Train=0.183183  Val=0.111032\n  Epoch  20/50  Train=0.172086  Val=0.103441\n  Epoch  30/50  Train=0.161571  Val=0.085615\n  Epoch  40/50  Train=0.159843  Val=0.084286\n  Epoch  50/50  Train=0.158319  Val=0.084328\n\n  ★ Best Val Loss: 0.083666\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x400 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"  📊 Training curve saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Extract Bottleneck Features ───────────────────────────\nprint(f\"\\n{'='*55}\")\nprint(f\"  EXTRACTING BOTTLENECK FEATURES\")\nprint(f\"{'='*55}\")\n \nmodel.eval()\nbottleneck_features = []\n \nfull_loader = DataLoader(TensorDataset(X_tensor), batch_size=BATCH_SIZE)\nwith torch.no_grad():\n    for (batch_x,) in full_loader:\n        batch_x = batch_x.to(DEVICE)\n        z       = model.encode(batch_x)\n        bottleneck_features.append(z.cpu().numpy())\n \nZ = np.concatenate(bottleneck_features, axis=0)\nprint(f\"\\n  Bottleneck shape : {Z.shape}\")\nprint(f\"  Records          : {Z.shape[0]:,}\")\nprint(f\"  Compressed dims  : {Z.shape[1]}\")\n \n# Name the bottleneck features\nbn_cols = [f'ae_feature_{i+1:02d}' for i in range(BOTTLENECK_DIM)]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:01.572816Z","iopub.execute_input":"2026-05-16T06:31:01.573147Z","iopub.status.idle":"2026-05-16T06:31:02.919163Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  EXTRACTING BOTTLENECK FEATURES\n=======================================================\n\n  Bottleneck shape : (248828, 20)\n  Records          : 248,828\n  Compressed dims  : 20\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Rank Original Features by Reconstruction Error ────────\nprint(f\"\\n{'='*55}\")\nprint(f\"  FEATURE IMPORTANCE VIA RECONSTRUCTION ERROR\")\nprint(f\"{'='*55}\")\n \n# Per-feature reconstruction error → higher error = more important\nmodel.eval()\nall_recon = []\nwith torch.no_grad():\n    for (batch_x,) in full_loader:\n        batch_x  = batch_x.to(DEVICE)\n        recon, _ = model(batch_x)\n        all_recon.append(recon.cpu().numpy())\n \nX_recon    = np.concatenate(all_recon, axis=0)\nfeat_error = np.mean((X_scaled - X_recon) ** 2, axis=0)\nfeat_importance = pd.Series(feat_error, index=feature_cols).sort_values(ascending=False)\n \nprint(f\"\\n  Feature Importance (by Reconstruction Error):\")\nprint(f\"  Higher error = more important (harder to reconstruct)\")\nprint()\nfor i, (feat, err) in enumerate(feat_importance.items(), 1):\n    bar    = '█' * int(err * 500)\n    marker = '✅' if i <= 20 else '  '\n    print(f\"  {marker} {i:2d}. {feat:35s}  err={err:.6f}  {bar}\")\n \n# Plot feature importance\nplt.figure(figsize=(10, 10))\ncolors = ['#2ecc71' if i < 20 else '#e74c3c'\n          for i in range(len(feat_importance))]\nfeat_importance.plot(kind='barh', color=colors[::-1])\nplt.axhline(y=len(feat_importance) - 20 - 0.5,\n            color='black', linestyle='--', linewidth=2,\n            label='Top 20 cutoff')\nplt.xlabel('Reconstruction Error (MSE)')\nplt.title('Feature Importance via Autoencoder Reconstruction Error')\nplt.legend()\nplt.tight_layout()\nplt.savefig('/kaggle/working/ae_feature_importance.png', dpi=150)\nplt.show()\nprint(\"\\n  📊 Feature importance plot saved\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:02.920107Z","iopub.execute_input":"2026-05-16T06:31:02.920478Z","iopub.status.idle":"2026-05-16T06:31:05.536590Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  FEATURE IMPORTANCE VIA RECONSTRUCTION ERROR\n=======================================================\n\n  Feature Importance (by Reconstruction Error):\n  Higher error = more important (harder to reconstruct)\n\n  ✅  1. is_dns_port                          err=0.874516  █████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\n  ✅  2. second                               err=0.467796  █████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\n  ✅  3. src_port                             err=0.368171  ████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\n  ✅  4. ip_oct4                              err=0.306449  █████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\n  ✅  5. minute                               err=0.256264  ████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\n  ✅  6. ip_class                             err=0.175608  ███████████████████████████████████████████████████████████████████████████████████████\n  ✅  7. dst_port                             err=0.146961  █████████████████████████████████████████████████████████████████████████\n  ✅  8. is_weekend                           err=0.127977  ███████████████████████████████████████████████████████████████\n  ✅  9. is_https_port                        err=0.120647  ████████████████████████████████████████████████████████████\n  ✅ 10. is_ntp_port                          err=0.115792  █████████████████████████████████████████████████████████\n  ✅ 11. is_db_port                           err=0.098483  █████████████████████████████████████████████████\n  ✅ 12. evt_outbound                         err=0.085789  ██████████████████████████████████████████\n  ✅ 13. is_mail_port                         err=0.082327  █████████████████████████████████████████\n  ✅ 14. is_registered_port                   err=0.078248  ███████████████████████████████████████\n  ✅ 15. ip_oct1                              err=0.078142  ███████████████████████████████████████\n  ✅ 16. result_status_unified                err=0.078015  ███████████████████████████████████████\n  ✅ 17. port_log                             err=0.077045  ██████████████████████████████████████\n  ✅ 18. is_remote_port                       err=0.071414  ███████████████████████████████████\n  ✅ 19. is_well_known_port                   err=0.068962  ██████████████████████████████████\n  ✅ 20. is_ephemeral_port                    err=0.067941  █████████████████████████████████\n     21. is_web_port                          err=0.066667  █████████████████████████████████\n     22. ip_oct2                              err=0.064517  ████████████████████████████████\n     23. ip_oct3                              err=0.062793  ███████████████████████████████\n     24. is_ssh_port                          err=0.062209  ███████████████████████████████\n     25. time_sin                             err=0.060315  ██████████████████████████████\n     26. evt_deny                             err=0.058794  █████████████████████████████\n     27. status_code_enc                      err=0.058332  █████████████████████████████\n     28. evt_scan                             err=0.049836  ████████████████████████\n     29. evt_ddos                             err=0.047002  ███████████████████████\n     30. day_of_week                          err=0.042237  █████████████████████\n     31. event_type_enc                       err=0.042096  █████████████████████\n     32. is_night                             err=0.041849  ████████████████████\n     33. evt_dos                              err=0.033841  ████████████████\n     34. data_classification_enc              err=0.032848  ████████████████\n     35. legal_basis_enc                      err=0.031004  ███████████████\n     36. evt_ssh                              err=0.029344  ██████████████\n     37. is_blocked                           err=0.027511  █████████████\n     38. approval_status_enc                  err=0.023630  ███████████\n     39. is_http_port                         err=0.023353  ███████████\n     40. is_private                           err=0.018907  █████████\n     41. time_cos                             err=0.017839  ████████\n     42. collection_method_enc                err=0.014877  ███████\n     43. legal_hold_enc                       err=0.014704  ███████\n     44. is_allowed                           err=0.014330  ███████\n     45. log_category_enc                     err=0.014177  ███████\n     46. hour                                 err=0.014145  ███████\n     47. evt_accept                           err=0.013977  ██████\n     48. is_business                          err=0.011035  █████\n     49. evt_inbound                          err=0.010745  █████\n     50. evt_vnet                             err=0.008148  ████\n     51. is_azure                             err=0.007173  ███\n     52. is_aws                               err=0.007173  ███\n     53. evt_vpc                              err=0.007173  ███\n     54. evt_bg                               err=0.000000  \n     55. is_loopback                          err=0.000000  \n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 1 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\n"},"metadata":{}},{"name":"stdout","text":"\n  📊 Feature importance plot saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Build Final Dataset (Bottleneck + Label) ───────────────\nprint(f\"\\n{'='*55}\")\nprint(f\"  BUILDING FINAL DATASET\")\nprint(f\"{'='*55}\")\n\nfinal_df = pd.DataFrame(Z, columns=bn_cols)\nfinal_df['label_multiclass'] = y\nfinal_df['cloud_platform']   = cloud_platform_values  # ← REPLACE if/platform block\n\nOUTPUT = '/kaggle/working/AUTOENCODER_FEATURES.csv'\nfinal_df.to_csv(OUTPUT, index=False)\n\nsize_mb = os.path.getsize(OUTPUT) / 1024 / 1024\nprint(f\"\\n  Records  : {len(final_df):,}\")\nprint(f\"  Features : {BOTTLENECK_DIM} (ae_feature_01 … ae_feature_20)\")\nprint(f\"  Label    : label_multiclass\")\nprint(f\"  Size     : {size_mb:.1f} MB\")\nprint(f\"  Saved    : {OUTPUT} ✅\")\n\n# Verify\nprint(f\"\\n  cloud_platform check:\")\nprint(final_df['cloud_platform'].value_counts().to_string())\n\ntorch.save(model.state_dict(), '/kaggle/working/autoencoder_weights.pth')\nprint(f\"  Model weights: autoencoder_weights.pth ✅\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:05.537709Z","iopub.execute_input":"2026-05-16T06:31:05.538065Z","iopub.status.idle":"2026-05-16T06:31:09.163474Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  BUILDING FINAL DATASET\n=======================================================\n\n  Records  : 248,828\n  Features : 20 (ae_feature_01 … ae_feature_20)\n  Label    : label_multiclass\n  Size     : 33.2 MB\n  Saved    : /kaggle/working/AUTOENCODER_FEATURES.csv ✅\n\n  cloud_platform check:\ncloud_platform\nAzure    146595\nAWS      102233\n  Model weights: autoencoder_weights.pth ✅\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Visualize Bottleneck Space ────────────────────────────\nprint(f\"\\n{'='*55}\")\nprint(f\"  VISUALISING BOTTLENECK FEATURE SPACE\")\nprint(f\"{'='*55}\")\n \n# 2D view of first 2 bottleneck dims coloured by class\nclass_names = {0:'DDoS', 1:'DoS', 2:'Normal', 3:'PortScan', 4:'SSH'}\ncolors_cls  = ['#e74c3c','#e67e22','#2ecc71','#3498db','#9b59b6']\n \n# Sample 5000 points for readable plot\nsample_idx  = np.random.choice(len(Z), min(5000, len(Z)), replace=False)\nZ_sample    = Z[sample_idx]\ny_sample    = y[sample_idx]\n \nplt.figure(figsize=(10, 7))\nfor cls_id, cls_name in class_names.items():\n    mask = y_sample == cls_id\n    plt.scatter(Z_sample[mask, 0], Z_sample[mask, 1],\n                c=colors_cls[cls_id], label=cls_name,\n                alpha=0.4, s=10)\nplt.xlabel('Bottleneck Feature 1')\nplt.ylabel('Bottleneck Feature 2')\nplt.title('Autoencoder Bottleneck Space (First 2 Dims)\\n'\n          'Coloured by Attack Class')\nplt.legend(markerscale=3)\nplt.grid(alpha=0.2)\nplt.tight_layout()\nplt.savefig('/kaggle/working/bottleneck_space.png', dpi=150)\nplt.show()\nprint(\"  📊 Bottleneck space plot saved\")\n \n# Bottleneck feature correlation\nplt.figure(figsize=(12, 10))\nsns.heatmap(pd.DataFrame(Z, columns=bn_cols).corr(),\n            cmap='coolwarm', center=0, annot=False,\n            square=True, linewidths=0.3)\nplt.title('Bottleneck Features Correlation Matrix')\nplt.tight_layout()\nplt.savefig('/kaggle/working/bottleneck_correlation.png', dpi=150)\nplt.show()\nprint(\"  📊 Bottleneck correlation heatmap saved\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:09.164593Z","iopub.execute_input":"2026-05-16T06:31:09.164827Z","iopub.status.idle":"2026-05-16T06:31:11.441055Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  VISUALISING BOTTLENECK FEATURE SPACE\n=======================================================\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x700 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"  📊 Bottleneck space plot saved\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x1000 with 2 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n3HhwoVGxwMAAAAAAJ3E6elFX0druwO1tCvP3SYrK0spKSnNHuuIHZ0AAAAAAGgvm8lkdIRvtE4tugwYMKAzb2eIgIAABQQEGB0DAAAAAAAYjDVdAAAAAAAAXICiCwAAAAAAgAtQdAEAAAAAAHABii4AAAAAAAAuQNEFAAAAAADABTp19yIAAAAAANB5bCbGWhiJtw8AAAAAAOACFF0AAAAAAABcgOlFAAAAAAB0UzaZjI7wjcZIFwAAAAAAABeg6AIAAAAAAOACJpvNZjM6BAAAAAAA6HjVfy0zOkK79Rs8wugIHY6RLgAAAAAAAC7AQrrd3KX0BUZHaNW9Wb+SJF0sP2xwkrYFDBklSTr/1/cNTtK6oMHRkqQLx48YnKRtgZExkqSTlVUGJ2ndoAfulyTVvLbc4CRt8/77dZKkU5XnDE7SuoEPhEqS6t59w+Akbev5/ackSR+fKjc4SevuGzhEknTxhaeNDdKGgBcLJEnX/rTN2CDt4DkpQZL0TmCUwUla98iFDyVJl19aZHCStvkte1WSdHVrlsFJWuc1N12SVHTPgwYnadtj109Ikq5s+AeDk7TO96f/KEmq277e4CSt6zk7VZJUW7rT4CRt6/XtmZK6ftabOT87tt/gJG3rM2y80RHQDTHSBQAAAAAAwAUY6QIAAAAAQDdlMzHWwki8fQAAAAAAABeg6AIAAAAAAOACTC8CAAAAAKCbsplMRkf4RmOkCwAAAAAAgAtQdAEAAAAAAHABii4AAAAAAAAuQNEFAAAAAADABSi6AAAAAAAAuABFFwAAAAAAABdgy2gAAAAAALopm9gy2kiMdAEAAAAAAHCBLld0ycjIUGBgoEwmk3bv3m10HAAAAAAAgDvSpYoux48fV2ZmpnJyclRdXa1HH320Q/rNyMjQiBEjOqSvjrB48WLFxMTIbDY3m+vs2bMymUy3ff7rv/6r88MCAAAAAO5aNpPbXfPpjrrUmi6VlZWSpPj4eJlMXW/emc1mU1NTk9zdv/5rmz9/vg4dOqRjx461eM7evXv10EMP2b/37dv3a98XAAAAAAB0DqdLSXv27NG4cePk5+envn37aurUqfZiiSR9/PHHeuKJJ+Tn56c+ffooPj5eZ8+ebbPfjIwMTZs27UYoNzeHokteXp4iIyPVs2dPDR48WK+99prDtcuWLdOgQYPk5eWliIgIpaWl6fr165KkgoICZWZm6ujRo/YRIwUFBfbRJGVlZfZ+Ll++LJPJpOLiYklScXGxTCaT3n77bfvIlJKSElmtVmVnZys8PFyenp4aPny4duzY0e53uGnTJj333HOKiIho9by+ffsqKCjI/rnnnnvafQ8AAAAAAGAsp4sutbW1Sk5O1uHDh/Xuu+/Kzc1Njz/+uKxWq65fv67Y2FhZLBbt379fBw4ckLe3t+Li4tTQ0NBqvykpKcrPz5ckVVdXq7q6WpK0bds2paen68UXX9Tx48e1du1apaWlaevWrfZrLRaLCgoKVF5erldeeUW5ubn6xS9+IUmaPXu2nn/+eT300EP2fmfPnu3UMy9fvlzr1q3T8ePHNWzYMGVnZ+uNN97Q5s2b9dFHHykpKUlPPvmk3nvvPaf6bcv06dMVEBCgcePG6Xe/+12H9g0AAAAAAFzL6Xkys2bNcvi+ZcsW+fv7q7y8XGVlZbJarcrLy7OPVMnPz5efn5+Ki4s1ZcqUFvv19vaWn5+fJCkoKMjevmrVKm3cuFEzZ86UJIWHh6u8vFw5OTmaO3euJGnlypX288PCwpSSkqLCwkKlpqbK09NT3t7ecnd3d+jXGVlZWXrkkUckSfX19Vq7dq327t2rsWPHSpIiIiJUUlKinJwcTZgw4Y7ucStvb29t3LhR3/nOd+Tm5qZ/+7d/04wZM7R7925Nnz79a/cPAAAAAABcz+miy6lTp5Senq5Dhw7p0qVLslqtkqSqqiodPXpUFRUVslgsDtfU1dU5TEFqr9raWlVWVmrBggVKTEy0tzc2NsrX19f+ffv27dq0aZMqKytVU1OjxsZG+fj4OH2/lowaNcr+c0VFha5evWovwtzU0NCg6OjoDrnfvffeq+TkZPv30aNH65NPPtGGDRtaLLrU19ervr7eoc1sNndIHgAAAAAA4Dyniy7Tpk1TaGiocnNzFRwcLKvVqqioKDU0NKimpkYxMTHatm3bbdf5+/s7Ha6mpkaSlJubqzFjxjgc69GjhyTp4MGDSkhIUGZmpmJjY+Xr66vCwkJt3Lix1b7d3G7MrLLZbPa2m+vAfFWvXr1uy1RUVKT+/fs7nOfKIseYMWP0zjvvtHg8OztbmZmZDm2rVq3SIpclAgAAAAAArXGq6PLpp5/qxIkTys3N1fjx4yVJJSUl9uMjR47U9u3bFRAQ0CEjTQIDAxUcHKzTp08rISGh2XNKS0sVGhqqF154wd527tw5h3M8PDzU1NTk0HazCFRdXW0foXLrorotGTJkiMxms6qqqjpkKlF7lZWVqV+/fi0eX7FihcPoGOlGEejLF//e1dEAAAAAAF2UTV1vZ+BvEqeKLr1791bfvn31+uuvq1+/fqqqqtLy5cvtxxMSErRhwwbFx8crKytLISEhOnfunHbu3KnU1FSFhIQ4HTAzM1OLFy+Wr6+v4uLiVF9fr8OHD+vzzz9XcnKyBg4cqKqqKhUWFmr06NEqKirSrl27HPoICwvTmTNnVFZWppCQEFksFnl6eurhhx/WunXrFB4erosXLzqsDdMSi8WilJQUJSUlyWq1aty4cbpy5YoOHDggHx8f+zozramoqFBNTY3Onz+va9eu2Ys9Q4YMkYeHh7Zu3SoPDw97MWjnzp3asmWL8vLyWuzTbDY3O9LmyzbTAAAAAAAAV3Bq9yI3NzcVFhbqyJEjioqKUlJSkjZs2GA/7uXlpX379un+++/XzJkzFRkZqQULFqiuru6OR74888wzysvLU35+voYOHaoJEyaooKBA4eHhkm7s8JOUlKRFixZpxIgRKi0tVVpamkMfs2bNUlxcnCZNmiR/f3+99dZbkm4sAtzY2KiYmBgtXbpUa9asaVem1atXKy0tTdnZ2YqMjFRcXJyKiorsmdrzTNHR0crJydHJkycVHR2t6OhoffLJJw73iImJ0ZgxY/Tb3/5W27dv17x589rVPwAAAAAAMJ7Ta7pMnjxZ5eXlDm23rosSFBTksJ2zM2bMmOHQ101z5szRnDlzWrxu/fr1Wr9+vUPb0qVL7T+bzWbt2LHjtusiIyNVWlrq0Hbr/SdOnNhsHpPJpCVLlmjJkiUtZmpNcXFxq8fnzp3brhEzAAAAAACg63K66AIAAAAAAO4ONpNTE1zQwTr17Xt7e7f42b9/f2dGcZmFCxe2+IwLFy40Oh4AAAAAAOgknTrSpbXdgb66/fLdKisrSykpKc0e64gdnQAAAAAAwN2hU4suAwYM6MzbGSIgIEABAQFGxwAAAAAAAAZjchcAAAAAAIALUHQBAAAAAABwAXYvAgAAAACgm7LJZHSEbzRGugAAAAAAALgARRcAAAAAAAAXoOgCAAAAAADgAqzpAgAAAABAN2UzMdbCSLx9AAAAAAAAF6DoAgAAAAAA4AIUXQAAAAAAAFyAogsAAAAAAIALmGw2m83oEAAAAAAAoOOdqawwOkK7hT8wwOgIHY6RLgAAAAAAdFM2me6az534p3/6J4WFhalnz54aM2aM/vznP7d6/m9+8xsNHjxYPXv21NChQ/X73//+ju7bXmwZ3c39aeBwoyO0atKpo5Kk6r+WGRukHfoNHiFJevv968YGacOj0fdIkv735AcGJ2lb/0FDJUmVp08bnKR1D0RESJL+cLTB4CRtmzLcQ5J0ZcM/GJykdb4//UdJ0vunLhmcpG3RA++VJF3dssrgJK3zmp8pSfoo/nsGJ2ndQ7/9oyTpVOU5g5O0beADoZKk2tyVBidpXa/ENZKkv62cZ3CStvmvyZckna6sNDhJ6yIeeEBS1/93qfR//z4tuudBg5O07rHrJyRJ1/74LwYnaZ3n9/5OklT3h3yDk7St55Qbv+e7+n+f3Pxvk66eU/q/rLh7bN++XcnJydq8ebPGjBmjX/7yl4qNjdWJEycUEBBw2/mlpaX6yU9+ouzsbE2dOlW//vWvNWPGDP3lL39RVFSUSzIy0gUAAAAAANx1fv7znysxMVHz5s3TkCFDtHnzZnl5eWnLli3Nnv/KK68oLi5OP/3pTxUZGanVq1dr5MiRevXVV12WkaILAAAAAAC4qzQ0NOjIkSOaPHmyvc3NzU2TJ0/WwYMHm73m4MGDDudLUmxsbIvndwSmFwEAAAAA0E3ZTHe2VooR6uvrVV9f79BmNptlNptvO/fSpUtqampSYGCgQ3tgYKD++te/Ntv/+fPnmz3//PnzXzN5yxjpAgAAAAAADJednS1fX1+HT3Z2ttGxvhZGugAAAAAAAMOtWLFCycnJDm3NjXKRpHvvvVc9evTQhQsXHNovXLigoKCgZq8JCgpy6vyOwEgXAAAAAABgOLPZLB8fH4dPS0UXDw8PxcTE6N1337W3Wa1Wvfvuuxo7dmyz14wdO9bhfEl65513Wjy/IzDSBQAAAAAA3HWSk5M1d+5cjRo1St/61rf0y1/+UrW1tZo378aW6k899ZT69+9vn6K0ZMkSTZgwQRs3btRjjz2mwsJCHT58WK+//rrLMlJ0AQAAAAAAd53Zs2frb3/7m9LT03X+/HmNGDFCe/bssS+WW1VVJTe3/5vg8+1vf1u//vWvtXLlSv3sZz/TwIEDtXv3bkVFRbksI0UXAAAAAABwV1q0aJEWLVrU7LHi4uLb2n70ox/pRz/6kYtT/R+KLgAAAAAAdFM2292zZXR3xEK6AAAAAAAALkDRBQAAAAAAwAW6XNElIyNDgYGBMplM2r17t9FxAAAAAAC4a9nkdtd8uqMu9VTHjx9XZmamcnJyVF1drUcffbRD+s3IyNCIESM6pK+OsHjxYsXExMhsNreYy2az6eWXX9agQYNkNpvVv39/vfjii50bFAAAAAAA3LEutZBuZWWlJCk+Pl4mU9db7Mdms6mpqUnu7l//tc2fP1+HDh3SsWPHmj2+ZMkS/eEPf9DLL7+soUOH6rPPPtNnn332te8LAAAAAAA6h9MjXfbs2aNx48bJz89Pffv21dSpU+3FEkn6+OOP9cQTT8jPz099+vRRfHy8zp4922a/GRkZmjZt2o1Qbm4ORZe8vDxFRkaqZ8+eGjx4sF577TWHa5ctW6ZBgwbJy8tLERERSktL0/Xr1yVJBQUFyszM1NGjR2UymWQymVRQUKCzZ8/KZDKprKzM3s/ly5dlMpns20oVFxfLZDLp7bffto9MKSkpkdVqVXZ2tsLDw+Xp6anhw4drx44d7X6HmzZt0nPPPaeIiIhmjx8/flz//M//rN/+9reaPn26wsPDFRMTo0ceeaTd9wAAAAAAAMZyuuhSW1ur5ORkHT58WO+++67c3Nz0+OOPy2q16vr164qNjZXFYtH+/ft14MABeXt7Ky4uTg0NDa32m5KSovz8fElSdXW1qqurJUnbtm1Tenq6XnzxRR0/flxr165VWlqatm7dar/WYrGooKBA5eXleuWVV5Sbm6tf/OIXkqTZs2fr+eef10MPPWTvd/bs2U498/Lly7Vu3TodP35cw4YNU3Z2tt544w1t3rxZH330kZKSkvTkk0/qvffec6rflvz7v/+7IiIi9B//8R8KDw9XWFiYnnnmGUa6AAAAAABwF3F6nsysWbMcvm/ZskX+/v4qLy9XWVmZrFar8vLy7CNV8vPz5efnp+LiYk2ZMqXFfr29veXn5ydJCgoKsrevWrVKGzdu1MyZMyVJ4eHhKi8vV05OjubOnStJWrlypf38sLAwpaSkqLCwUKmpqfL09JS3t7fc3d0d+nVGVlaWfZRJfX291q5dq71792rs2LGSpIiICJWUlCgnJ0cTJky4o3vc6vTp0zp37px+85vf6I033lBTU5OSkpL0wx/+UH/84x+/dv8AAAAAAMD1nC66nDp1Sunp6Tp06JAuXbokq9UqSaqqqtLRo0dVUVEhi8XicE1dXZ3DFKT2qq2tVWVlpRYsWKDExER7e2Njo3x9fe3ft2/frk2bNqmyslI1NTVqbGyUj4+P0/dryahRo+w/V1RU6OrVq7dN9WloaFB0dHSH3M9qtaq+vl5vvPGGBg0aJEn61a9+pZiYGJ04cUIPPvjgbdfU19ervr7eoc1sNndIHgAAAAAA4Dyniy7Tpk1TaGiocnNzFRwcLKvVqqioKDU0NKimpkYxMTHatm3bbdf5+/s7Ha6mpkaSlJubqzFjxjgc69GjhyTp4MGDSkhIUGZmpmJjY+Xr66vCwkJt3Lix1b7d3G7MrLLZbPa2m+vAfFWvXr1uy1RUVKT+/fs7nNdRRY5+/frJ3d3dXnCRpMjISEk3ilvNFV2ys7OVmZnp0LZq1Sp9/XE3AAAAAIC7lU1db5OabxKnii6ffvqpTpw4odzcXI0fP16SVFJSYj8+cuRIbd++XQEBAR0y0iQwMFDBwcE6ffq0EhISmj2ntLRUoaGheuGFF+xt586dczjHw8NDTU1NDm03i0DV1dX2ESq3LqrbkiFDhshsNquqqqpDphI15zvf+Y4aGxtVWVmpBx54QJJ08uRJSVJoaGiz16xYsULJyckObWazWaXbdrkkIwAAAAAAaJ1TRZfevXurb9++ev3119WvXz9VVVVp+fLl9uMJCQnasGGD4uPjlZWVpZCQEJ07d047d+5UamqqQkJCnA6YmZmpxYsXy9fXV3Fxcaqvr9fhw4f1+eefKzk5WQMHDlRVVZUKCws1evRoFRUVadcux0JDWFiYzpw5o7KyMoWEhMhiscjT01MPP/yw1q1bp/DwcF28eNFhbZiWWCwWpaSkKCkpSVarVePGjdOVK1d04MAB+fj42NeZaU1FRYVqamp0/vx5Xbt2zV7sGTJkiDw8PDR58mSNHDlS8+fP1y9/+UtZrVY999xzeuSRRxxGv9zKbDYznQgAAAAAgC7Eqd2L3NzcVFhYqCNHjigqKkpJSUnasGGD/biXl5f27dun+++/XzNnzlRkZKQWLFigurq6Ox758swzzygvL0/5+fkaOnSoJkyYoIKCAoWHh0uSpk+frqSkJC1atEgjRoxQaWmp0tLSHPqYNWuW4uLiNGnSJPn7++utt96SdGMR4MbGRsXExGjp0qVas2ZNuzKtXr1aaWlpys7OVmRkpOLi4lRUVGTP1J5nio6OVk5Ojk6ePKno6GhFR0frk08+kXTjPf/7v/+77r33Xn33u9/VY489psjISBUWFrb3tQEAAAAAIJtMd82nO3J6TZfJkyervLzcoe3WdVGCgoIctnN2xowZMxz6umnOnDmaM2dOi9etX79e69evd2hbunSp/Wez2awdO3bcdl1kZKRKS0sd2m69/8SJE5vNYzKZtGTJEi1ZsqTFTK0pLi5u85zg4GD927/92x31DwAAAAAAjOfUSBcAAAAAAAC0T6cWXby9vVv87N+/vzOjuMzChQtbfMaFCxcaHQ8AAAAAAHQSp6cXfR2t7Q701e2X71ZZWVlKSUlp9lhH7OgEAAAAAADuDp1adBkwYEBn3s4QAQEBCggIMDoGAAAAAAAwGGu6AAAAAAAAuECnjnQBAAAAAACdp7tuxXy3YKQLAAAAAACAC1B0AQAAAAAAcAGmFwEAAAAA0E0xvchYjHQBAAAAAABwAYouAAAAAAAALkDRBQAAAAAAwAVY0wUAAAAAgG7KZmNNFyMx0gUAAAAAAMAFKLoAAAAAAAC4gMlms9mMDgEAAAAAADpeecUnRkdotyEDgo2O0OEY6QIAAAAAAOACLKTbzX355yKjI7TK8q3HJEl/rfwfg5O0bfADIZKk2tKdBidpXa9vz5QkfXl4j8FJ2mYZFSdJKjv1N4OTtG7EQH9JXf/3k/R/v6fOVpw0OEnrwgYMkiT9beU8g5O0zX9NviTpyMnPDE7SuphBfSRJX/zlHYOTtM5n5COSpLq3XjI4Sdt6/mSZJKnunQJjg7Sh5yNPS5I+z/57Y4O0Q+8Vr0mSzlRWGJykdeEPDJAk1W1fb3CStvWcnSpJuvbHfzE4Ses8v/d3kqSiex40OEnrHrt+QpL0X2O+ZXCStj186M+Suv6v05u/Ru+m/44COhJFFwAAAAAAuimb2L3ISEwvAgAAAAAAcAGKLgAAAAAAAC5A0QUAAAAAAMAFWNMFAAAAAIBuijVdjMVIFwAAAAAAABeg6AIAAAAAAOACFF0AAAAAAABcgKILAAAAAACAC1B0AQAAAAAAcAF2LwIAAAAAoJti9yJjMdIFAAAAAADABSi6AAAAAAAAuECXK7pkZGQoMDBQJpNJu3fvNjoOAAAAAADAHelSRZfjx48rMzNTOTk5qq6u1qOPPtoh/WZkZGjEiBEd0ldHWLx4sWJiYmQ2m5vNlZGRIZPJdNunV69enR8WAAAAAHDXstlMd82nO+pSC+lWVlZKkuLj42Uydb0XbrPZ1NTUJHf3r//a5s+fr0OHDunYsWO3HUtJSdHChQsd2r7//e9r9OjRX/u+AAAAAACgczg90mXPnj0aN26c/Pz81LdvX02dOtVeLJGkjz/+WE888YT8/PzUp08fxcfH6+zZs232m5GRoWnTpt0I5ebmUHTJy8tTZGSkevbsqcGDB+u1115zuHbZsmUaNGiQvLy8FBERobS0NF2/fl2SVFBQoMzMTB09etQ+YqSgoEBnz56VyWRSWVmZvZ/Lly/LZDKpuLhYklRcXCyTyaS3337bPjKlpKREVqtV2dnZCg8Pl6enp4YPH64dO3a0+x1u2rRJzz33nCIiIpo97u3traCgIPvnwoULKi8v14IFC9p9DwAAAAAAYCynh2zU1tYqOTlZw4YNU01NjdLT0/X444+rrKxMTU1Nio2N1dixY7V//365u7trzZo1iouL07Fjx+Th4dFivykpKQoLC9O8efNUXV1tb9+2bZvS09P16quvKjo6Wu+//74SExPVq1cvzZ07V5JksVhUUFCg4OBgffDBB0pMTJTFYlFqaqpmz56tDz/8UHv27NHevXslSb6+vrpw4UK7n3n58uV6+eWXFRERod69eys7O1tvvvmmNm/erIEDB2rfvn168skn5e/vrwkTJjj7StuUl5enQYMGafz48R3eNwAAAAAAcA2niy6zZs1y+L5lyxb5+/urvLxcZWVlslqtysvLs49Uyc/Pl5+fn4qLizVlypQW+/X29pafn58kKSgoyN6+atUqbdy4UTNnzpQkhYeHq7y8XDk5Ofaiy8qVK+3nh4WFKSUlRYWFhUpNTZWnp6e8vb3l7u7u0K8zsrKy9Mgjj0iS6uvrtXbtWu3du1djx46VJEVERKikpEQ5OTkdXnSpq6vTtm3btHz58lbPq6+vV319vUOb2Wzu0CwAAAAAAKD9nC66nDp1Sunp6Tp06JAuXbokq9UqSaqqqtLRo0dVUVEhi8XicE1dXZ3DFKT2qq2tVWVlpRYsWKDExER7e2Njo3x9fe3ft2/frk2bNqmyslI1NTVqbGyUj4+P0/dryahRo+w/V1RU6OrVq/YizE0NDQ2Kjo7usHvetGvXLn355Zf2AlNLsrOzlZmZ6dC2atUqPf8D1oEBAAAAAMAIThddpk2bptDQUOXm5io4OFhWq1VRUVFqaGhQTU2NYmJitG3bttuu8/f3dzpcTU2NJCk3N1djxoxxONajRw9J0sGDB5WQkKDMzEzFxsbK19dXhYWF2rhxY6t9u7ndWM7GZrPZ226uA/NVt+4adDNTUVGR+vfv73CeK0aW5OXlaerUqQoMDGz1vBUrVig5Ofm2PA1H93Z4JgAAAAAA0Danii6ffvqpTpw4odzcXPv6IiUlJfbjI0eO1Pbt2xUQENAhI00CAwMVHBys06dPKyEhodlzSktLFRoaqhdeeMHedu7cOYdzPDw81NTU5NB2swhUXV1tH6Fy66K6LRkyZIjMZrOqqqpcsn7Lrc6cOaM//elP+t3vftfmuWazudmiT4MrggEAAAAA7gpWdb2dgb9JnCq69O7dW3379tXrr7+ufv36qaqqymGtkYSEBG3YsEHx8fHKyspSSEiIzp07p507dyo1NVUhISFOB8zMzNTixYvl6+uruLg41dfX6/Dhw/r888+VnJysgQMHqqqqSoWFhRo9erSKioq0a9cuhz7CwsJ05swZlZWVKSQkRBaLRZ6ennr44Ye1bt06hYeH6+LFiw5rw7TEYrEoJSVFSUlJslqtGjdunK5cuaIDBw7Ix8enzWlA0o0pSjU1NTp//ryuXbtmL/YMGTLEYbHhLVu2qF+/fnr00Uede2kAAAAAAMBwTm0Z7ebmpsLCQh05ckRRUVFKSkrShg0b7Me9vLy0b98+3X///Zo5c6YiIyO1YMEC1dXV3fHIl2eeeUZ5eXnKz8/X0KFDNWHCBBUUFCg8PFySNH36dCUlJWnRokUaMWKESktLlZaW5tDHrFmzFBcXp0mTJsnf319vvfWWpBtFjcbGRsXExGjp0qVas2ZNuzKtXr1aaWlpys7OVmRkpOLi4lRUVGTP1J5nio6OVk5Ojk6ePKno6GhFR0frk08+sZ9jtVpVUFCgp59+2j6VCgAAAAAA3D2cXtNl8uTJKi8vd2i7dV2UoKAgbd269Y7CzJgxw6Gvm+bMmaM5c+a0eN369eu1fv16h7alS5fafzabzdqxY8dt10VGRqq0tNSh7db7T5w4sdk8JpNJS5Ys0ZIlS1rM1Jri4uI2z3Fzc9PHH398R/0DAAAAACBJNqYXGcqpkS4AAAAAAABon04tunh7e7f42b9/f2dGcZmFCxe2+IwLFy40Oh4AAAAAAOgkTk8v+jpa2x3oq9sv362ysrKUkpLS7LGO2NEJAAAAAADcHTq16DJgwIDOvJ0hAgICFBAQYHQMAAAAAABgMNZ0AQAAAAAAcAGKLgAAAAAAAC7QqdOLAAAAAABA57HZ2DLaSIx0AQAAAAAAcAGKLgAAAAAAAC7A9CIAAAAAALopm5heZCRGugAAAAAAALgARRcAAAAAAAAXoOgCAAAAAADgAhRdAAAAAAAAXICiCwAAAAAAgAuYbDabzegQAAAAAACg4x05+ZnREdotZlAfoyN0OLaMBgAAAACgm7LZ2DLaSBRdurnag7uNjtCqXmNnSJJOVH5sbJB2ePCB+yRJtaU7DU7Sul7fnilJuvpeocFJ2uY14ceSpPdPXTI4SeuiB94rSar+a5mxQdqh3+ARkqQvD+8xNkgbLKPiJEl1b71kcJK29fzJMknS/5z80OAkrQsZFCVJulh+2OAkrQsYMkqSdOnDgwYnadu9UWMlSX8r/7PBSVrnP+RbkqSLK54yOEnbArLfkCSdqzhhcJLWhQ54UFLX/zNf+r8/9+v+kG9wktb1nDJPkvRfY75lcJLWPXzoxu/3onseNDhJ2x67fuP3Ud3uTQYnaV3PGYslSdf+tM3gJG3znJRgdAR0Q6zpAgAAAAAA4AKMdAEAAAAAoJuyielFRmKkCwAAAAAAgAtQdAEAAAAAAHABii4AAAAAAAAuQNEFAAAAAADABSi6AAAAAAAAuABFFwAAAAAAABdgy2gAAAAAALopm40to43ESBcAAAAAAAAXoOgCAAAAAADgAkwvAgAAAACgm7IaHeAbrsuNdMnIyFBgYKBMJpN2795tdBwAAAAAAIA70qWKLsePH1dmZqZycnJUXV2tRx99tEP6zcjI0IgRIzqkr46wePFixcTEyGw2t5jrP//zP/Xwww/LYrHI399fs2bN0tmzZzs1JwAAAAAAuHNdquhSWVkpSYqPj1dQUJDMZrPBiRzZbDY1NjZ2SF/z58/X7Nmzmz125swZxcfH63vf+57Kysr0n//5n7p06ZJmzpzZIfcGAAAAAACu53TRZc+ePRo3bpz8/PzUt29fTZ061V4skaSPP/5YTzzxhPz8/NSnTx/Fx8e3a4RGRkaGpk2bdiOUm5tMpv/b1iovL0+RkZHq2bOnBg8erNdee83h2mXLlmnQoEHy8vJSRESE0tLSdP36dUlSQUGBMjMzdfToUZlMJplMJhUUFOjs2bMymUwqKyuz93P58mWZTCYVFxdLkoqLi2UymfT222/bR6aUlJTIarUqOztb4eHh8vT01PDhw7Vjx452v8NNmzbpueeeU0RERLPHjxw5oqamJq1Zs0YPPPCARo4cqZSUFJWVldmfCwAAAAAAdG1OF11qa2uVnJysw4cP691335Wbm5sef/xxWa1WXb9+XbGxsbJYLNq/f78OHDggb29vxcXFqaGhodV+U1JSlJ+fL0mqrq5WdXW1JGnbtm1KT0/Xiy++qOPHj2vt2rVKS0vT1q1b7ddaLBYVFBSovLxcr7zyinJzc/WLX/xCkjR79mw9//zzeuihh+z9tjTCpCXLly/XunXrdPz4cQ0bNkzZ2dl64403tHnzZn300UdKSkrSk08+qffee8+pflsSExMjNzc35efnq6mpSVeuXNG//Mu/aPLkybrnnns65B4AAAAAAMC1nN69aNasWQ7ft2zZIn9/f5WXl6usrExWq1V5eXn2kSr5+fny8/NTcXGxpkyZ0mK/3t7e8vPzkyQFBQXZ21etWqWNGzfap9aEh4ervLxcOTk5mjt3riRp5cqV9vPDwsKUkpKiwsJCpaamytPTU97e3nJ3d3fo1xlZWVl65JFHJEn19fVau3at9u7dq7Fjx0qSIiIiVFJSopycHE2YMOGO7nGr8PBw/eEPf9ATTzyh//f//p+ampo0duxY/f73v//afQMAAAAAgM7hdNHl1KlTSk9P16FDh3Tp0iVZrTc2oKqqqtLRo0dVUVEhi8XicE1dXZ3DFKT2qq2tVWVlpRYsWKDExER7e2Njo3x9fe3ft2/frk2bNqmyslI1NTVqbGyUj4+P0/dryahRo+w/V1RU6OrVq/YizE0NDQ2Kjo7ukPudP39eiYmJmjt3rn7yk5/oyy+/VHp6un74wx/qnXfecZh6dVN9fb3q6+sd2rramjgAAAAAgM5ls93+/4/oPE4XXaZNm6bQ0FDl5uYqODhYVqtVUVFRamhoUE1NjWJiYrRt27bbrvP393c6XE1NjSQpNzdXY8aMcTjWo0cPSdLBgweVkJCgzMxMxcbGytfXV4WFhdq4cWOrfbu53ZhZZbPZ7G0trZfSq1ev2zIVFRWpf//+Dud1VJHjn/7pn+Tr66v169fb2958803dd999OnTokB5++OHbrsnOzlZmZqZD26pVq/TT2BEdkgkAAAAAADjHqaLLp59+qhMnTig3N1fjx4+XJJWUlNiPjxw5Utu3b1dAQECHjDQJDAxUcHCwTp8+rYSEhGbPKS0tVWhoqF544QV727lz5xzO8fDwUFNTk0PbzSJQdXW1fYTKrYvqtmTIkCEym82qqqrqkKlEzbl69aq9KHTTzSLTzZFFX7VixQolJyc7tJnNZjX+5W2XZAQAAAAAAK1zqujSu3dv9e3bV6+//rr69eunqqoqLV++3H48ISFBGzZsUHx8vLKyshQSEqJz585p586dSk1NVUhIiNMBMzMztXjxYvn6+iouLk719fU6fPiwPv/8cyUnJ2vgwIGqqqpSYWGhRo8eraKiIu3atcuhj7CwMJ05c0ZlZWUKCQmRxWKRp6enHn74Ya1bt07h4eG6ePGiw9owLbFYLEpJSVFSUpKsVqvGjRunK1eu6MCBA/Lx8bGvM9OaiooK1dTU6Pz587p27Zq92DNkyBB5eHjoscce0y9+8QtlZWXZpxf97Gc/U2hoaItTmMxmc7MjbTpmg2sAAAAAwN3IJqYXGcmp3Yvc3NxUWFioI0eOKCoqSklJSdqwYYP9uJeXl/bt26f7779fM2fOVGRkpBYsWKC6uro7HvnyzDPPKC8vT/n5+Ro6dKgmTJiggoIChYeHS5KmT5+upKQkLVq0SCNGjFBpaanS0tIc+pg1a5bi4uI0adIk+fv766233pJ0YxHgxsZGxcTEaOnSpVqzZk27Mq1evVppaWnKzs5WZGSk4uLiVFRUZM/UnmeKjo5WTk6OTp48qejoaEVHR+uTTz6RJH3ve9/Tr3/9a+3evVvR0dGKi4uT2WzWnj175Onp2d5XBwAAAAAADOT0mi6TJ09WeXm5Q9ut66IEBQU5bOfsjBkzZjj0ddOcOXM0Z86cFq9bv369w/onkrR06VL7z2azWTt27LjtusjISJWWljq03Xr/iRMnNpvHZDJpyZIlWrJkSYuZWlNcXNzmOT/+8Y/14x//+I76BwAAAAAAxnNqpAsAAAAAAADap1OLLt7e3i1+9u/f35lRXGbhwoUtPuPChQuNjgcAAAAAADqJ09OLvo7Wdgf66vbLd6usrCylpKQ0e6wjdnQCAAAAAAB3h04tugwYMKAzb2eIgIAABQQEGB0DAAAAAAAYrFOLLgAAAAAAoPPYbGwZbSQW0gUAAAAAAHABii4AAAAAAAAuQNEFAAAAAADABVjTBQAAAACAbsom1nQxEiNdAAAAAAAAXICiCwAAAAAAgAtQdAEAAAAAAHABii4AAAAAAAAuQNEFAAAAAADABdi9CAAAAACAbspqMzrBN5vJZrPxjwAAAAAAgG5o30e1Rkdot+8+1MvoCB2O6UUAAAAAAAAuwPSibu69j64aHaFVEx7ykiQdOfmZwUnaFjOojyTpL98fZ3CS1o18t0SS9GHFeYOTtC1qQJAkqaS8a1ffxw25UXE/+ZM4g5O0bdBbeyRJvzvcZHCS1k0f1UOSdGHZ3xmcpG2BL/2LJOlAeY3BSVr3nSHekrr+32bd/BusSx8eNDhJ2+6NGitJ+uyDEoOTtK7P0Bt/Ll3d/xuDk7TNa/yPJEknKj82OEnrHnzgPklSbelOg5O0rde3Z0qS/nC0weAkrZsy3EOSVLd9vcFJWtdzdqokqW73JoOTtK3njMWSpKJ7HjQ4Seseu35CknRm/nSDk7QtfMvvjI6AboiiCwAAAAAA3ZRNJqMjfKMxvQgAAAAAAHRbn332mRISEuTj4yM/Pz8tWLBANTUtj2L+7LPP9A//8A968MEH5enpqfvvv1+LFy/WlStXnL43RRcAAAAAANBtJSQk6KOPPtI777yj//iP/9C+ffv07LPPtnj+J598ok8++UQvv/yyPvzwQxUUFGjPnj1asGCB0/dmehEAAAAAAOiWjh8/rj179ui///u/NWrUKEnSP/7jP+oHP/iBXn75ZQUHB992TVRUlP7t3/7N/v2BBx7Qiy++qCeffFKNjY1yd29/KYWRLgAAAAAAoFs6ePCg/Pz87AUXSZo8ebLc3Nx06NChdvdz5coV+fj4OFVwkRjpAgAAAAAAuoD6+nrV19c7tJnNZpnN5jvu8/z58woICHBoc3d3V58+fXT+fPt2fL106ZJWr17d6pSkljDSBQAAAAAAGC47O1u+vr4On+zs7GbPXb58uUwmU6ufv/71r1870xdffKHHHntMQ4YMUUZGhtPXM9IFAAAAAIBuyma7e7aMXrFihZKTkx3aWhrl8vzzz+vpp59utb+IiAgFBQXp4sWLDu2NjY367LPPFBQU1Or1X375peLi4mSxWLRr1y7dc889bT/EV1B0AQAAAAAAhnNmKpG/v7/8/f3bPG/s2LG6fPmyjhw5opiYGEnSH//4R1mtVo0ZM6bF67744gvFxsbKbDbrd7/7nXr27Nm+h/gKphcBAAAAAIBuKTIyUnFxcUpMTNSf//xnHThwQIsWLdKPf/xj+85F//u//6vBgwfrz3/+s6QbBZcpU6aotrZWv/rVr/TFF1/o/PnzOn/+vJqampy6PyNdAAAAAADopmw2oxMYb9u2bVq0aJG+//3vy83NTbNmzdKmTZvsx69fv64TJ07o6tWrkqS//OUv9p2NBgwY4NDXmTNnFBYW1u57U3QBAAAAAADdVp8+ffTrX/+6xeNhYWGy3VKdmjhxosP3r4PpRQAAAAAAAC7Q5YouGRkZCgwMlMlk0u7du42OAwAAAAAAcEe6VNHl+PHjyszMVE5Ojqqrq/Xoo492SL8ZGRkaMWJEh/TVERYvXqyYmBiZzeYWc/3rv/6rRowYIS8vL4WGhmrDhg2dGxIAAAAAAHwtXWpNl8rKSklSfHy8TKaut5e4zWZTU1OT3N2//mubP3++Dh06pGPHjt127O2331ZCQoL+8R//UVOmTNHx48eVmJgoT09PLVq06GvfGwAAAAAAuJ7TI1327NmjcePGyc/PT3379tXUqVPtxRJJ+vjjj/XEE0/Iz89Pffr0UXx8vM6ePdtmvxkZGZo2bdqNUG5uDkWXvLw8RUZGqmfPnho8eLBee+01h2uXLVumQYMGycvLSxEREUpLS9P169clSQUFBcrMzNTRo0dlMplkMplUUFCgs2fPymQyqayszN7P5cuXZTKZVFxcLEkqLi6WyWTS22+/bR+ZUlJSIqvVquzsbIWHh8vT01PDhw/Xjh072v0ON23apOeee04RERHNHv+Xf/kXzZgxQwsXLlRERIQee+wxrVixQi+99FKHLeYDAAAAAABcy+khG7W1tUpOTtawYcNUU1Oj9PR0Pf744yorK1NTU5NiY2M1duxY7d+/X+7u7lqzZo3i4uJ07NgxeXh4tNhvSkqKwsLCNG/ePFVXV9vbt23bpvT0dL366quKjo7W+++/r8TERPXq1Utz586VJFksFhUUFCg4OFgffPCBEhMTZbFYlJqaqtmzZ+vDDz/Unj17tHfvXkmSr6+vLly40O5nXr58uV5++WVFRESod+/eys7O1ptvvqnNmzdr4MCB2rdvn5588kn5+/trwoQJzr7S29TX18vLy8uhzdPTU//zP/+jc+fOObU9FQAAAADgm8uqrjeL5JvE6aLLrFmzHL5v2bJF/v7+Ki8vV1lZmaxWq/Ly8uwjVfLz8+Xn56fi4mJNmTKlxX69vb3l5+cnSQoKCrK3r1q1Shs3btTMmTMlSeHh4SovL1dOTo696LJy5Ur7+WFhYUpJSVFhYaFSU1Pl6ekpb29vubu7O/TrjKysLD3yyCOSbhRE1q5dq71792rs2LGSpIiICJWUlCgnJ6dDii6xsbFKSkrS008/rUmTJqmiokIbN26UJFVXVzdbdKmvr1d9fb1Dm9ls/tpZAAAAAADAnXG66HLq1Cmlp6fr0KFDunTpkqxWqySpqqpKR48eVUVFhSwWi8M1dXV1DlOQ2qu2tlaVlZVasGCBEhMT7e2NjY3y9fW1f9++fbs2bdqkyspK1dTUqLGxUT4+Pk7fryWjRo2y/1xRUaGrV6/aizA3NTQ0KDo6ukPul5iYqMrKSk2dOlXXr1+Xj4+PlixZooyMDLm5NT8jLDs7W5mZmQ5tq1at0qQfpXZIJgAAAAAA4Byniy7Tpk1TaGiocnNzFRwcLKvVqqioKDU0NKimpkYxMTHatm3bbdf5+/s7Ha6mpkaSlJubqzFjxjgc69GjhyTp4MGDSkhIUGZmpmJjY+Xr66vCwkL7yJCW3Cxe3LpGys11YL6qV69et2UqKipS//79Hc7rqJElJpNJL730ktauXavz58/L399f7777riS1uA7MihUrlJycfFue/6po6pBMAAAAAIC7j83G9CIjOVV0+fTTT3XixAnl5uZq/PjxkqSSkhL78ZEjR2r79u0KCAjokJEmgYGBCg4O1unTp5WQkNDsOaWlpQoNDdULL7xgbzt37pzDOR4eHmpqciw+3CwCVVdX20eo3LqobkuGDBkis9msqqqqDplK1JoePXrYCztvvfWWxo4d22Lxymw2t1D0uerChAAAAAAAoCVOFV169+6tvn376vXXX1e/fv1UVVWl5cuX248nJCRow4YNio+PV1ZWlkJCQnTu3Dnt3LlTqampCgkJcTpgZmamFi9eLF9fX8XFxam+vl6HDx/W559/ruTkZA0cOFBVVVUqLCzU6NGjVVRUpF27djn0ERYWpjNnzqisrEwhISGyWCzy9PTUww8/rHXr1ik8PFwXL150WBumJRaLRSkpKUpKSpLVatW4ceN05coVHThwQD4+PvZ1ZlpTUVGhmpoanT9/XteuXbMXe4YMGSIPDw9dunRJO3bs0MSJE1VXV6f8/Hz95je/0Xvvvef0+wMAAAAAAMZwastoNzc3FRYW6siRI4qKilJSUpI2bNhgP+7l5aV9+/bp/vvv18yZMxUZGakFCxaorq7ujke+PPPMM8rLy1N+fr6GDh2qCRMmqKCgQOHh4ZKk6dOnKykpSYsWLdKIESNUWlqqtLQ0hz5mzZqluLg4TZo0Sf7+/nrrrbck3VgEuLGxUTExMVq6dKnWrFnTrkyrV69WWlqasrOzFRkZqbi4OBUVFdkzteeZoqOjlZOTo5MnTyo6OlrR0dH65JNP7Ods3bpVo0aN0ne+8x199NFHKi4u1re+9a129Q8AAAAAAIzn9JoukydPVnl5uUPbreuiBAUFaevWrXcUZsaMGQ593TRnzhzNmTOnxevWr1+v9evXO7QtXbrU/rPZbNaOHTtuuy4yMlKlpaUObbfef+LEic3mMZlMWrJkiZYsWdJiptYUFxe3evzee+/VwYMH76hvAAAAAADQNTg10gUAAAAAAADt06lFF29v7xY/+/fv78woLrNw4cIWn3HhwoVGxwMAAAAAAJ3E6elFX0druwN9dfvlu1VWVpZSUlKaPdYROzoBAAAAANBezayYgU7UqUWXAQMGdObtDBEQEKCAgACjYwAAAAAAAIOxpgsAAAAAAIALdOpIFwAAAAAA0HlsMhkd4RuNkS4AAAAAAAAuQNEFAAAAAADABSi6AAAAAAAAuABFFwAAAAAAABeg6AIAAAAAAOACFF0AAAAAAABcgC2jAQAAAADopqw2oxN8szHSBQAAAAAAwAUougAAAAAAALiAyWazMdgIAAAAAIBu6O33rxsdod0ejb7H6AgdjpEuAAAAAAAALsBCut1cSXmt0RFaNW5IL0nSuYoTBidpW+iAByVJNf+UanCS1nk/t16S9MVf3jE4Sdt8Rj4iSfqg4oLBSVo3dECgJKlq4UyDk7Tt/s07JUm7/txkcJLWPf6tHpKkTz8sNThJ2/pGfVuS9KcPrhmcpHWThnpK6vp/m3Xzb7DeP3XJ4CRtix54ryTpTGWFwUlaF/7AAEnS/5z80OAkbQsZFCVJOl1ZaXCS1kU88IAk6bNj+w1O0rY+w8ZLkv5wtMHgJK2bMtxDkvTln4sMTtI6y7cekyRd+9M2g5O0zXNSgiTpzPzpBidpXfiW30mSiu550OAkbXvsetf/fxLcfRjpAgAAAAAA4AIUXQAAAAAAAFyAogsAAAAAAIALUHQBAAAAAABwARbSBQAAAACgm7LZjE7wzcZIFwAAAAAAABeg6AIAAAAAAOACTC8CAAAAAKCbsspkdIRvNEa6AAAAAAAAuABFFwAAAAAAABeg6AIAAAAAAOACFF0AAAAAAABcoMsVXTIyMhQYGCiTyaTdu3cbHQcAAAAAAOCOdKmiy/Hjx5WZmamcnBxVV1fr0Ucf7ZB+MzIyNGLEiA7p6+s6evSofvKTn+i+++6Tp6enIiMj9corr9x2XnFxsUaOHCmz2awBAwaooKCg88MCAAAAAIA71qW2jK6srJQkxcfHy2Tqetta2Ww2NTU1yd39zl/bkSNHFBAQoDfffFP33XefSktL9eyzz6pHjx5atGiRJOnMmTN67LHHtHDhQm3btk3vvvuunnnmGfXr10+xsbEd9TgAAAAAgG7OZjM6wTeb0yNd9uzZo3HjxsnPz099+/bV1KlT7cUSSfr444/1xBNPyM/PT3369FF8fLzOnj3bZr8ZGRmaNm3ajVBubg5Fl7y8PEVGRqpnz54aPHiwXnvtNYdrly1bpkGDBsnLy0sRERFKS0vT9evXJUkFBQXKzMzU0aNHZTKZZDKZVFBQoLNnz8pkMqmsrMzez+XLl2UymVRcXCzpxmgTk8mkt99+WzExMTKbzSopKZHValV2drbCw8Pl6emp4cOHa8eOHe16f/Pnz9crr7yiCRMmKCIiQk8++aTmzZunnTt32s/ZvHmzwsPDtXHjRkVGRmrRokX64Q9/qF/84hftugcAAAAAADCe00WX2tpaJScn6/Dhw3r33Xfl5uamxx9/XFarVdevX1dsbKwsFov279+vAwcOyNvbW3FxcWpoaGi135SUFOXn50uSqqurVV1dLUnatm2b0tPT9eKLL+r48eNau3at0tLStHXrVvu1FotFBQUFKi8v1yuvvKLc3Fx7gWL27Nl6/vnn9dBDD9n7nT17tlPPvHz5cq1bt07Hjx/XsGHDlJ2drTfeeEObN2/WRx99pKSkJD355JN67733nOr3pitXrqhPnz727wcPHtTkyZMdzomNjdXBgwfvqH8AAAAAAND5nJ4nM2vWLIfvW7Zskb+/v8rLy1VWViar1aq8vDz7SJX8/Hz5+fmpuLhYU6ZMabFfb29v+fn5SZKCgoLs7atWrdLGjRs1c+ZMSVJ4eLjKy8uVk5OjuXPnSpJWrlxpPz8sLEwpKSkqLCxUamqqPD095e3tLXd3d4d+nZGVlaVHHnlEklRfX6+1a9dq7969Gjt2rCQpIiJCJSUlysnJ0YQJE5zqu7S0VNu3b1dRUZG97fz58woMDHQ4LzAwUF988YWuXbsmT0/PO3oOAAAAAADQeZwuupw6dUrp6ek6dOiQLl26JKvVKkmqqqrS0aNHVVFRIYvF4nBNXV2dwxSk9qqtrVVlZaUWLFigxMREe3tjY6N8fX3t37dv365NmzapsrJSNTU1amxslI+Pj9P3a8moUaPsP1dUVOjq1av2IsxNDQ0Nio6OdqrfDz/8UPHx8Vq1alWrBan2qK+vV319vUOb2Wz+Wn0CAAAAAO5uNlvXWy/1m8Tposu0adMUGhqq3NxcBQcHy2q1KioqSg0NDaqpqVFMTIy2bdt223X+/v5Oh6upqZEk5ebmasyYMQ7HevToIenGVJyEhARlZmYqNjZWvr6+Kiws1MaNG1vt283txswq2y2rCt1cB+arevXqdVumoqIi9e/f3+E8Z4oc5eXl+v73v69nn33WYaSOdGOkz4ULFxzaLly4IB8fnxZHuWRnZyszM9OhbdWqVZr8xE/bnQkAAAAAAHQcp4oun376qU6cOKHc3FyNHz9eklRSUmI/PnLkSG3fvl0BAQEdMtIkMDBQwcHBOn36tBISEpo9p7S0VKGhoXrhhRfsbefOnXM4x8PDQ01NTQ5tN4tA1dXV9hEqty6q25IhQ4bIbDarqqrK6alEN3300Uf63ve+p7lz5+rFF1+87fjYsWP1+9//3qHtnXfesU9nas6KFSuUnJzs0GY2m/XflY13lBEAAAAAAHw9ThVdevfurb59++r1119Xv379VFVVpeXLl9uPJyQkaMOGDYqPj1dWVpZCQkJ07tw57dy5U6mpqQoJCXE6YGZmphYvXixfX1/FxcWpvr5ehw8f1ueff67k5GQNHDhQVVVVKiws1OjRo1VUVKRdu3Y59BEWFqYzZ86orKxMISEhslgs8vT01MMPP6x169YpPDxcFy9evG3ESXMsFotSUlKUlJQkq9WqcePG6cqVKzpw4IB8fHzs68y05MMPP9T3vvc9xcbGKjk5WefPn5d0Y+TOzULQwoUL9eqrryo1NVXz58/XH//4R/3rv/6rw7ovX2U2m1sYaUPRBQAAAAAAIzi1e5Gbm5sKCwt15MgRRUVFKSkpSRs2bLAf9/Ly0r59+3T//fdr5syZioyM1IIFC1RXV3fHI1+eeeYZ5eXlKT8/X0OHDtWECRNUUFCg8PBwSdL06dOVlJSkRYsWacSIESotLVVaWppDH7NmzVJcXJwmTZokf39/vfXWW5JuLALc2NiomJgYLV26VGvWrGlXptWrVystLU3Z2dmKjIxUXFycioqK7Jlas2PHDv3tb3/Tm2++qX79+tk/o0ePtp8THh6uoqIivfPOOxo+fLg2btyovLw8xcbGtve1AQAAAAAAgzm9psvkyZNVXl7u0HbruihBQUEO2zk7Y8aMGQ593TRnzhzNmTOnxevWr1+v9evXO7QtXbrU/rPZbNaOHTtuuy4yMlKlpaUObbfef+LEic3mMZlMWrJkiZYsWdJippZkZGQoIyOjzfMmTpyo999/3+n+AQAAAABA1+B00QUAAAAAANwdrLePI0Ancmp60dfl7e3d4mf//v2dGcVlFi5c2OIzLly40Oh4AAAAAACgk3TqSJfWdgf66vbLd6usrCylpKQ0e6wjdnQCAAAAAAB3h04tugwYMKAzb2eIgIAABQQEGB0DAAAAAAAYjDVdAAAAAADopprZGwadqFPXdAEAAAAAAPimoOgCAAAAAADgAhRdAAAAAAAAXICiCwAAAAAAgAtQdAEAAAAAAHABdi8CAAAAAKCbsslkdIRvNEa6AAAAAAAAuABFFwAAAAAAABeg6AIAAAAAAOACrOkCAAAAAEA3ZbUZneCbzWSz2fhHAAAAAABAN7TjkNXoCO32wzHdbzJO93siuEx9fb0yMjJUX19vdJQ23S1Z75ac0t2TlZwd727JerfklO6erHdLTunuyUrOjne3ZL1bckp3T9a7Jad092S9W3JKd1dWfLMx0gXt9sUXX8jX11dXrlyRj4+P0XFadbdkvVty6v9r787Dq6oP/I9/7uVCQjbQCCSgGCIgRPRhc4ZtwA1QrNalrQoKrWLHTn10tDLV0Q5g3dCZulWZUVqWwYX5VWwLZbFi2EXZgrJD2NRgdFRQUYLcfH5/8OTq9SaB6XOScz/4eT/PeeCcL3+8yJN7z80333MOdKx2Bp+KVcUJ6FhVnICO1c7gU7GqOAEdq4oT0LGqOAEta9h5pUu4HX//I+ecc84555xzzrk0yJMuzjnnnHPOOeeccw2QJ12cc84555xzzjnnGiA/MtodcxkZGRg7diwyMjLCphw1FauKE9Cx2hl8KlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKxh57u4hptvpOucc84555xzzh2n/b8VOjfS/WGf4+9inOPvf+Scc84555xzzjmXBnmli3POOeecc84551wD5JUuzjnnnHPOOeeccw2QJ12cc84555xzzjnnGiBPujjnnHPOOeecc841QJ50cc4dUwsXLsSXX34ZNuO4qaqqCuXl5aiqqgqbclxVWVmJ999/P2xGncXjcVRWVuLDDz8Mm1Jv+/fvx5YtW7Blyxbs378/bI4LKZKIx+NhM46pKVOm+Hs1wLZt24YFCxZg+/btYVOk+/br580338SKFSvS8ty/Z88evPHGG1i5ciU++uijsDlHraqqKi2/js7Vlidd3N/Upk2bUFxcHDYj0bp163Dffffh6aefxv/+7/8mjX366ae4/vrrQ5IlN2nSJIwaNQqTJ08GAMyYMQNdu3ZFcXExxo4dG7Ku/oYMGYJdu3aFzUjqgw8+SNovKyvDqFGj0L9/f/zgBz/AwoULw4F9qylTpuD1118HABw8eBA33HADsrOz0blzZ+Tk5OCmm25Kmw8OZ555Jn7961/jnXfeCZtSbx9//DF+8IMfoH379vjZz36GeDyO0aNHo7CwEO3atUO/fv2wd+/esJmJ/vKXv2DgwIHIzs5G27ZtUVBQgJYtW+K6667Dnj17wuYlmjRpEkpKSnDiiSeipKQk6e+/+93vwuYdc+vWrUOTJk3CZgAA5syZg9GjR+Nf/uVfsHnz5qSxTz75BOedd15Isq87fPgw7rnnHgwaNChxLnrkkUeQk5ODrKwsjBo1CocOHQpZWX8//elPUVFRETYj0Ztvvpn0A/fs2bMxaNAgtGvXDr1798a0adNC1CX34IMPYsGCBQCOfE9ecMEFOP300zF48GCcfvrpuOiii7Bv375wkQByc3Nxww03YPny5WFTjtru3bvRu3dvZGRk4KKLLsKnn36KwYMHo0+fPujXrx9KSkqwdevWsJkAgKeffhqnnnoqOnTogH79+qFPnz5o3bo1BgwYgNWrV4fNS+qvf/0rhg0bhhNOOAFZWVnIysrCCSecgGHDhuHVV18Nm+dc3dG5v6GysjJGo9GwGSTJ+fPns1mzZjzjjDPYvn175ufn87XXXkuMv//++2lhffTRR5mdnc0rrriChYWFvO+++5ifn8/77ruP48ePZ15eHv/rv/4rbCZ79OhR6xaJRNi1a9fEfjoUjUZZWVlJkly2bBmbNm3KQYMGccyYMRw8eDBjsRgXLVoUspLs0KEDV6xYQZK84447WFRUxJkzZ3LTpk384x//yM6dO3PMmDEhK48UiUSYn5/PJk2acOjQofzDH/7Ar776KmxWStdffz27devGJ598koMGDeL3v/99nnXWWVy6dCmXL1/Os88+myNHjgybSZKcNm0ac3Nz+Ytf/IJ33303CwoKeOedd3LixIkcNGgQTzrpJG7dujVsJh9++GFmZWXxzjvvZGlpKTdu3MiNGzeytLSUd911F7Ozs/nII4+EzTymysrKGIlEwmbwueeeY5MmTXjxxRdzwIABzMzM5PTp0xPj6XJ+uueee9imTRvefvvtLCkp4U033cRTTjmF06dP59SpU9muXTtOmDAhbCZJ8oQTTqh1i0QibNGiRWI/7L55fvrzn//MaDTKkSNH8qmnnuLo0aMZi8U4c+bMkJVHOvnkk7lmzRqS5OjRo9mjRw+uWbOGX375JcvKytinTx/ecMMNISuPnJ/OOOMMRiIRdunShf/+7//ODz74IGxWrV155ZUcNGgQZ82axR/96Efs378/zznnHL777rusqKjg0KFDedlll4XN5COPPMK2bdvyySef5LPPPsuuXbvy3nvv5dy5c3ndddcxKyuLK1euDJtJkpwyZQpjsRivvvpqTp48mXPmzOGcOXM4efJkXnPNNWzatCmnTZsWNtO5WvOki6u12267rd7t2muvTYsPiiTZt29f/uu//itJsrq6mhMmTGBOTg7nzp1LMn0+1Hbp0oXPPfccSXLNmjWMxWKcNGlSYnzSpEns1atXWLxEsViMF154IceNG5fYxo4dy2g0yn/6p39KHEuHIpFI4kPt4MGDef311yeN33rrrTzvvPPCoCWVkZHB3bt3kyQ7d+6c+N6sadGiRWzfvn0YtJQikQjfe+89vvzyy7zkkksYi8XYqlUr/uIXv+DGjRvD5iUqLCzksmXLSB55jUciEb7yyiuJ8aVLl7Jdu3Zh8ZLq0qULX3zxxcT+ypUrefLJJ7O6upokedVVV/Hyyy8Pi5eoffv2nDFjRp3jL774Ik855ZRGFNXd5ZdfXu923nnnpcX7fvfu3fn4448n9mfMmMHs7OzEe3+6nJ+Ki4s5a9YskuS2bdsYjUaTvmdnzJjBbt26hcVLKicnhxdffDGnTJmS2CZPnswmTZrw/vvvTxwLu2+enwYMGMA777wzafz+++9nnz59wqCllJGRwV27dpEki4qKUn5ZsWrVKhYWFoZBS6rma1pWVsabb76ZJ554Ips1a8YrrriCc+bMSbynpkOtWrXi2rVrSZL79u1jJBLhkiVLEuOrV69mmzZtQtJ9XVFREefMmZPY37JlC/Pz8xO/bLnllls4ePDgsHhJderUib/97W/rHH/qqafYsWPHRhQ5d+x50sXVWjQaZc+ePXnOOefUuvXu3TstPiiSZF5eHrdv35507LnnnmN2djZnzZqVNh9qmzdvnvjBmzzyIWf9+vWJ/W3btrFly5Zh0JJaunQpTzvtNP7bv/0b4/F44ngsFuOGDRtClKX2zQ+1hYWFfP3115PG169fz5NOOikMWlKnnnpqYvVVu3btUn5rtHHjRmZnZ4dBS+mbX1OSrKio4AMPPMBOnToxGo2yb9++/N3vfhei8EhZWVmJHxJIsmnTpnz77bcT+zt27Eibr2nz5s25c+fOpGOxWIzvvfceSfKNN95Ii9d+ZmZmvRNrGzZsYPPmzRtRVHexWIwXXXQRf/zjH9e6XXrppWnxvp+dnc0dO3YkHXvttdeYk5PDiRMnps35KTMzk3v27Ena37RpU2J/x44dzM3NDYOW0rZt2xIr2T777LPE8XQ7R33zvbR169ZctWpV0vjmzZvT4nVPHvllwOzZs0keWZlZM6Fd09q1a5mXlxcGLalvn58OHjzI559/nueffz6j0ShPPvlk/upXvwpR+HW5ubmJ1348HmcsFmNZWVlifNu2bWnxmsrKyko6P1VXVzMWi7GiooLkkVWDOTk5IemSy8jI4ObNm+sc37x5MzMzMxtR5Nyx53u6uFrr2LEjbrvtNpSWlta6Pfvss2ETE2VkZKRcazx8+HBMmjQJV111FV5++eVwYN8qKysLBw4cSOy3atUKOTk5Sf/m8OHDjc1KqX///li9ejW2bt2Kfv36oby8PGxSvX322Wf49NNPkZmZiYyMjKSxzMxMfPHFFyHJvm7EiBG4++67sW/fPlx33XW499578fnnnwMAvvjiC4wbNw79+/cPWXmkSCSStF9YWIi77roLW7duxYIFC3DaaafhlltuCUn3dZ06dcLs2bMBAHPnzkVmZiZeeeWVxPj8+fPRoUOHsHhJFRUVYdWqVYn9NWvWIBqNok2bNgCAE088EV999VVYvERnn302HnrooVrfh+LxOCZMmICzzz47BFlqXbt2xZVXXonJkyfXuo0fPz5sIgAgLy8PlZWVScfOPfdczJ49G2PGjMGTTz4Zkiy5Fi1aJJ1He/bsidzc3MR+VVVVyntDWHXs2BHLly9HQUEBunfvjmXLloVNqrONGzfirbfeQvPmzVFdXZ0yng7nfAC48cYbMWbMGGzfvh0333wz7rjjjsS5f+fOnbjtttswZMiQkJWp56eMjAxcc801ePXVV1FeXo4f//jHmDJlSji4b3XGGWfg97//PQBg6tSpyM/Px4svvpgYf+GFF9C5c+eweIk6d+6Mv/71r4n90tJSNGvWDAUFBQCOfI5Kl9f+GWecUe+9xX7/+9+jpKSkEUXOHXuxsAEuPevduzdWr16Na6+9ttbxSCQCko2sqr3u3bujtLQUvXr1Sjp+9dVXgyRGjRoVkiy5Ll264K233kLXrl0BIOVGpZs3b0ZRUVEIstRatGiBF154AZMnT8aAAQMwfvz4tDnpfruaDy0ksWrVKvTo0SMxtmHDBrRt2zYsWqKxY8di/fr1KC4uRu/evbFkyRK0adMG7dq1Q0VFBfLz85M+9IRZfa/rc845B+eccw4+/fTTRhTV3pgxYzBq1Cg89thjeOeddzB9+nTceuuteOONNxCNRjFz5kz85je/CZsJAPj5z3+O0aNHY+XKlcjMzMSkSZNw3XXXJW70+sYbb6TFh+/f/va3GDp0KAoKCjBw4MDEpFBlZSUWL16MZs2aJU1shVmvXr2wZs0a3HDDDbWOZ2RkoH379o2sSu3v/u7vMHfuXPTp0yfp+KBBgzBr1ix873vfC0mWXElJCdasWYMzzzwTAFImMt5++2106tQpDFqtxWIxTJgwAUOHDsXw4cMxYsSItDxHnX/++Yn31GXLliVNWq5duzYtvkcB4I477sCePXtQUlKC0047Dbt27ULnzp0Ri8Vw+PBh9OzZEy+88ELYzHrPT0VFRVCrBpYAABm1SURBVPj1r3+Ne++9txFFdTdu3DhcdtllePjhhxGNRjF//nzceOONeO211xCNRrFy5Uo8//zzYTNx11134dprr8Wrr76KzMxMzJw5E7fcckvi9bRw4UJ069YtZOWR/uM//gPf+973MG/ePFxwwQVJ56gFCxZgx44d+Mtf/hKy0rnaizBdfnJ2adX777+PqqoqnHrqqWFTjtrLL7+MxYsX49FHH611/Pnnn8ezzz6L0tLSRpYlt2zZMmRnZ6N79+61jj/99NOorq7GzTff3Liwo7Rt2zaMGDECq1atwvr169PqtwiLFi1K2i8sLEz64fXxxx/HoUOHMGbMmMam1dq8efMwa9Ys7NixA9XV1SgsLET//v0xfPhwZGdnh80DAPzkJz/BE088kfRb7nRt2bJlWLFiBfr27Yt+/fph48aNeOihh/DFF1/gkksuSZsJVwCYOHEipk+fjqqqKgwdOhS/+tWvkJmZCeDIaywej6NLly4hK4+sHJs+fTpWrFiRePR2QUEB+vbti+HDhyMvLy9k4ZGqqqoQj8eRlZUVNqXeFi1ahOXLl+Ouu+6qdby0tBTTpk1LPNEurLZu3YqmTZvWuTrs+eefRywWw49+9KNGlh29jz76CDfeeCNKS0uxYsUKnH766WGTABx5es03y8nJQX5+fmK/5ulFI0eObFRXfW3atAmzZ89OOUddcMEFaTGpNX78eIwZMybtX/c17dq1C6tXr0avXr1QVFSEyspKPPXUU/jiiy9w8cUX49xzzw2bCODIatFvnp9uvPHGxFjNo6O/+b0bZrt27cLEiRNrPUfddNNNafPLS+e+nSddnHNHrbq6Gp999hny8vLS4oOXc84555xzzinky4tcvR0+fBgbNmxImk0uKSlB06ZNQ5alpmK1M/hUrCpOQMeq4gS0rLX11VdfYe/evWlzSUR9HT58GBUVFWlvtTP4VKwqTkDnta/0NVWxqjidS/vCuoOvS+/i8TjvvvtutmzZkpFIJGlr2bIl77nnnqQn24SZitXO4FOxqjhJHauKk9Sy1ldZWVlaPGnnWFKx2hl8KlYVJ6ljVXGSOtZ0cz711FM8//zz+cMf/pCvvvpq0tiHH37IDh06hCRzrv789CJXa3feeSeeeeYZPPTQQ9ixYwcOHDiAAwcOYMeOHZgwYQKeeeaZOq9Rb+xUrHYGn4pVxQnoWFWcgJbVOeecS8eeeOIJjBkzBl26dEFGRgaGDRuGBx98MDEej8dT7qXkXLrke7q4WisoKMDUqVMxdOjQWsfnz5+PkSNHpjwKM4xUrHYGn4pVxQnoWFWcgI61Z8+e9Y5/+eWX2Lp1K+LxeCOJ6k7FamfwqVhVnICOVcUJ6FhVnMCRR0bffffdGD58OABg+fLluOyyy3DTTTfh3nvvRWVlJdq2bZsWVue+ne/p4mrts88+q/dRu4WFhThw4EAjiupOxWpn8KlYVZyAjlXFCehYN27ciKuvvrrOJ9js3bsXW7dubWRV7alY7Qw+FauKE9CxqjgBHauKEwB27tyJfv36Jfb79euH1157DRdccAG++uor/PM//3N4OOeOVtjXN7n0bNiwYRwyZAg//PDDlLEPP/yQF154IS+++OIQZKmpWO0MPhWripPUsao4SR1rr169+PTTT9c5vnbt2rS5tl/FamfwqVhVnKSOVcVJ6lhVnCR5yimncPHixSnHN2zYwDZt2nDkyJFpY3Xu23mli6u1//zP/8SwYcNQWFiIM888E23atAEAVFZW4u2330ZJSQlmz54dsvJIKlY7g0/FquIEdKwqTkDH2r9/f2zZsqXO8dzcXAwcOLARRXWnYrUz+FSsKk5Ax6riBHSsKk4AGDBgAGbOnIl/+Id/SDpeUlKCBQsW4Nxzzw1J5tzR8z1dXJ1VV1dj/vz5WLFiRdIjTvv27YshQ4YgGk2f+zCrWO0MPhWrihPQsao4AS2rc845l2699dZbWL16NX7yk5/UOr5+/Xq89NJLGDt2bCPLnDuGwl5q446Pfvazn9W6dD4dU7HaGXwqVhUnqWNVcZI6VhUnqWO1M/hUrCpOUseq4iR1rCpOUsvqjv+80sUFUl5eHsrKylBcXBw25aipWO0MPhWrihPQsao4AR2rihPQsdoZfCpWFSegY1VxAjpWFSegZXXHf17P7AJJae5OxWpn8KlYVZyAjlXFCehYVZyAjtXO4FOxqjgBHauKE9CxqjgBLas7/vOki3POOeecc84551wD5EkX55xzzjnnnHPOuQbIky7OOeecc84555xzDZAnXZxzzjnnnHPOOecaIE+6uEC69tprkZeXFzbjmFKx2hl8KlYVJ6BjVXECOlYVJ6BjtTP4VKwqTkDHquIEdKwqTkDL6r4DNebzqZ1mixcv5ogRI9inTx++++67JMlp06ZxyZIlIctSU7HaGXwqVhUnqWNVcZI6VhUnqWO1M/hUrCpOUseq4iR1rCpOUsvqHEl6pYurt5deeglDhw5F8+bNsXbtWlRVVQEA9u/fjwceeCBkXXIqVjuDT8Wq4gR0rCpOQMeq4gR0rHYGn4pVxQnoWFWcgI5VxQloWZ1LFPasj0vvunfvzqlTp5Ikc3JyWF5eTpJcs2YN27RpEyYtJRWrncGnYlVxkjpWFSepY1VxkjpWO4NPxariJHWsKk5Sx6riJLWsztXklS6u3rZs2YKBAwemHG/RogX27dvX+KB6UrHaGXwqVhUnoGNVcQI6VhUnoGO1M/hUrCpOQMeq4gR0rCpOQMvqXE2edHH1VlBQgO3bt6ccX7p0KYqLi0MQ1Z2K1c7gU7GqOAEdq4oT0LGqOAEdq53Bp2JVcQI6VhUnoGNVcQJaVucShb3UxqV3DzzwAEtKSrhixQrm5uZyyZIlnD59Olu1asUnnngibF5SKlY7g0/FquIkdawqTlLHquIkdax2Bp+KVcVJ6lhVnKSOVcVJalmdq8mTLq7eqqured999zE7O5uRSISRSISZmZm85557wqalpGK1M/hUrCpOUseq4iR1rCpOUsdqZ/CpWFWcpI5VxUnqWFWcpJbVuZoiJBn2ahuXnsXjcSxbtgxnnXUWsrKysH37dnz++ecoKSlBTk5O2LykVKx2Bp+KVcUJ6FhVnICOVcUJ6FjtDD4Vq4oT0LGqOAEdq4oT0LI698086eLqLTMzE5s2bUKHDh3Cphw1FaudwadiVXECOlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKzO1eQb6bp669atG3bs2BE245hSsdoZfCpWFSegY1VxAjpWFSegY7Uz+FSsKk5Ax6riBHSsKk5Ay+pconCvbnLp3ty5c9m9e3fOmjWLFRUV3L9/f9KWTqlY7Qw+FauKk9SxqjhJHauKk9Sx2hl8KlYVJ6ljVXGSOlYVJ6llda4mX17k6i0a/XoxVCQSSfydJCKRCOLxeBisWlOx2hl8KlYVJ6BjVXECOlYVJ6BjtTP4VKwqTkDHquIEdKwqTkDL6lxNsbABLr0rLS0Nm3DMqVjtDD4Vq4oT0LGqOAEdq4oT0LHaGXwqVhUnoGNVcQI6VhUnoGV1riavdHHOOeecc84555xrgLzSxdXb4sWL6x0fOHBgI0mOnorVzuBTsao4AR2rihPQsao4AR2rncGnYlVxAjpWFSegY1VxAlpW52ryShdXb9+8brKmb14/mU7XTapY7Qw+FauKE9CxqjgBHauKE9Cx2hl8KlYVJ6BjVXECOlYVJ6Blda4mPzLa1dsnn3yStH3wwQeYN28ezj77bLzyyith85JSsdoZfCpWFSegY1VxAjpWFSegY7Uz+FSsKk5Ax6riBHSsKk5Ay+pcosZ6TJI7vlq4cCF79uwZNuOYUrHaGXwqVhUnqWNVcZI6VhUnqWO1M/hUrCpOUseq4iR1rCpOUsvqvnt5pYv7m2rTpg22bNkSNuOYUrHaGXwqVhUnoGNVcQI6VhUnoGO1M/hUrCpOQMeq4gR0rCpOQMvqvnv5Rrqu3t56662kfZLYu3cvHnroIXTv3j0cVB2pWO0MPhWrihPQsao4AR2rihPQsdoZfCpWFSegY1VxAjpWFSegZXUuUeMurHFqRSIRRqNRRiKRpK1v377ctGlT2LykVKx2Bp+KVcVJ6lhVnKSOVcVJ6ljtDD4Vq4qT1LGqOEkdq4qT1LI6V5OfXuTqbffu3Un70WgUrVq1QmZmZkiiulOx2hl8KlYVJ6BjVXECOlYVJ6BjtTP4VKwqTkDHquIEdKwqTkDL6lyisGd9XHo3depUHjx4MOV4VVUVp06dGoKo7lSsdgafilXFSepYVZykjlXFSepY7Qw+FauKk9SxqjhJHauKk9SyOleTV7q4emvSpAn27t2L1q1bJx3/6KOP0Lp1a8Tj8ZBkqalY7Qw+FauKE9CxqjgBHauKE9Cx2hl8KlYVJ6BjVXECOlYVJ6Blda4mP73I1RtJRCKRlOPvvvsuWrRoEYKo7lSsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOleTn17kaq1Hjx6IRCKIRCI4//zzEYt9/a0Sj8exc+dOXHjhhSEKv07FamfwqVhVnICOVcUJ6FhVnICO1c7gU7GqOAEdq4oT0LGqOAEtq3PfzpMurtYuu+wyAEBZWRmGDh2KnJycxFizZs1QVFSEK6+8MiRdcipWO4NPxariBHSsKk5Ax6riBHSsdgafilXFCehYVZyAjlXFCWhZnUupMW8g4/SaMmUKv/zyy7AZx5SK1c7gU7GqOEkdq4qT1LGqOEkdq53Bp2JVcZI6VhUnqWNVcZJaVudq8o10nXPOOeecc8455xogX17k6i0ej+PRRx/F//zP/2DPnj04dOhQ0vjHH38ckiw1FaudwadiVXECOlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKzO1eSnF7l6Gz9+PH7zm9/gqquuwv79+3H77bfjiiuuQDQaxbhx48LmJaVitTP4VKwqTkDHquIEdKwqTkDHamfwqVhVnICOVcUJ6FhVnICW1blEYV/f5NK74uJizp49mySZk5PD7du3kyQff/xxXnPNNWHSUlKx2hl8KlYVJ6ljVXGSOlYVJ6ljtTP4VKwqTlLHquIkdawqTlLL6lxNnnRx9ZaVlcXdu3eTJAsKCrh69WqSZHl5OfPy8sKkpaRitTP4VKwqTlLHquIkdawqTlLHamfwqVhVnKSOVcVJ6lhVnKSW1bmafHmRq7eTTz4Ze/fuBQCcdtppeOWVVwAAK1euREZGRpi0lFSsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOpco7Fkfl9798pe/5P3330+SfPHFFxmLxdixY0c2a9aMv/zlL0PWJaditTP4VKwqTlLHquIkdawqTlLHamfwqVhVnKSOVcVJ6lhVnKSW1bma/Mho93/q9ddfx+uvv45OnTrhkksuCZtTbypWO4NPxariBHSsKk5Ax6riBHSsdgafilXFCehYVZyAjlXFCWhZ3Xc3T7o455xzzjnnnHPONUC+p4s7av/93/+N/v37o23btti9ezcA4LHHHsOf/vSnkGWpqVjtDD4Vq4oT0LGqOAEdq4oT0LHaGXwqVhUnoGNVcQI6VhUnoGV1DvCkiztKEydOxO23345hw4Zh3759iMfjAICWLVviscceCxf3rVSsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOpco3FvKuHSva9eufPnll0mSOTk5LC8vJ0m+/fbbzM/PD1GWmorVzuBTsao4SR2ripPUsao4SR2rncGnYlVxkjpWFSepY1VxklpW52ryShdXbzt37kSPHj1SjmdkZODAgQMhiOpOxWpn8KlYVZyAjlXFCehYVZyAjtXO4FOxqjgBHauKE9CxqjgBLatzNXnSxdVbhw4dUFZWlnJ83rx56Nq1a+OD6knFamfwqVhVnICOVcUJ6FhVnICO1c7gU7GqOAEdq4oT0LGqOAEtq3M1xcIGuPTu9ttvx89//nMcPHgQJPHmm2/ihRdewIMPPohJkyaFzUtKxWpn8KlYVZyAjlXFCehYVZyAjtXO4FOxqjgBHauKE9CxqjgBLatziRr3aian2PTp09mxY0dGIhFGIhG2a9eOkyZNCptVaypWO4NPxariJHWsKk5Sx6riJHWsdgafilXFSepYVZykjlXFSWpZnSNJT7q4lP70pz/x0KFDKccPHDjAysrKEER1p2K1M/hUrCpOUseq4iR1rCpOUsdqZ/CpWFWcpI5VxUnqWFWcpJbVudqKkGTYq21cetWkSRO8//77aNWqFZo0aYK9e/eidevWYbNqTcVqZ/CpWFWcgI5VxQnoWFWcgI7VzuBTsao4AR2rihPQsao4AS2rc7XlG+m6lFq1aoUVK1YAAEgiEomELKo7FaudwadiVXECOlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKzO1VpjLqtxGo0dO5aRSITRaPSoW9ipWO387lpVnEpWFaeSVcWpZLXzu2tVcSpZVZxKVhWnmtW52vLlRa7WNm/ejO3bt+PSSy/F5MmT0bJly1r/3fe///3GhdWSitXO4FOxqjgBHauKE9CxqjgBHaudwadiVXECOlYVJ6BjVXECWlbnUgp71seld+PGjeOBAweO+u+WLl3KgwcPNoKo7lSsdgafilXFSepYVZykjlXFSepY7Qw+FauKk9SxqjhJHauKk9SyOleTV7q4QMrLy0NZWRmKi4vDphw1FaudwadiVXECOlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKzu+M830nWBpDR3p2K1M/hUrCpOQMeq4gR0rCpOQMdqZ/CpWFWcgI5VxQnoWFWcgJbVHf950sU555xzzjnnnHOuAfKki3POOeecc84551wD5EkX55xzzjnnnHPOuQbIky4ukCKRSNiEY07FamfwqVhVnICOVcUJ6FhVnICO1c7gU7GqOAEdq4oT0LGqOAEtqzv+86SLCySlm1WpWO0MPhWrihPQsao4AR2rihPQsdoZfCpWFSegY1VxAjpWFSegZXXHf35ktDtqhw8fxsKFC1FeXo7hw4cjNzcXFRUVyMvLQ05OTti8pFSsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOgcAoHP1tGvXLnbp0oVZWVls0qQJy8vLSZK33HIL//Ef/zFkXXIqVjuDT8Wq4iR1rCpOUseq4iR1rHYGn4pVxUnqWFWcpI5VxUlqWZ2ryZcXuXq79dZb0bt3b3zyySdo3rx54vjll1+OBQsWhChLTcVqZ/CpWFWcgI5VxQnoWFWcgI7VzuBTsao4AR2rihPQsao4AS2rczXFwga49G7JkiVYvnw5mjVrlnS8qKgI7733Xkiq2lOx2hl8KlYVJ6BjVXECOlYVJ6BjtTP4VKwqTkDHquIEdKwqTkDL6lxNXuni6q26uhrxeDzl+Lvvvovc3NwQRHWnYrUz+FSsKk5Ax6riBHSsKk5Ax2pn8KlYVZyAjlXFCehYVZyAltW5mjzp4uptyJAheOyxxxL7kUgEn3/+OcaOHYthw4aFB6slFaudwadiVXECOlYVJ6BjVXECOlY7g0/FquIEdKwqTkDHquIEtKzOJQr7pjIuvXvnnXdYUlLCrl27MhaLsU+fPszPz2fnzp1ZWVkZNi8pFaudwadiVXGSOlYVJ6ljVXGSOlY7g0/FquIkdawqTlLHquIktazO1eRHRrujdvjwYcyYMQPr1q3D559/jp49e2LEiBFJN69Kl1SsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOgcAnnRxx9TGjRuxZ88eHDp0KOn4pZdeGpKo7lSsdgafilXFCehYVZyAjlXFCehY7Qw+FauKE9CxqjgBHauKE9CyOufLi1y9lZeX86yzzmIkEmE0Gk38WbOlUypWO4NPxariJHWsKk5Sx6riJHWsdgafilXFSepYVZykjlXFSWpZnavJN9J19XbrrbeiQ4cO+OCDD5CVlYX169dj0aJF6N27NxYuXBg2LykVq53Bp2JVcQI6VhUnoGNVcQI6VjuDT8Wq4gR0rCpOQMeq4gS0rM4lCnvWx6V3+fn5XLduHUkyLy+PmzdvJkkuWLCA3bt3D5OWkorVzuBTsao4SR2ripPUsao4SR2rncGnYlVxkjpWFSepY1VxklpW52ryShdXb/F4PPHM+5NOOgkVFRUAgFNPPRVbtmwJk5aSitXO4FOxqjgBHauKE9CxqjgBHaudwadiVXECOlYVJ6BjVXECWlbnaoqFDXDpXbdu3bBu3Tp06NABf//3f4+HH34YzZo1wzPPPIPi4uKweUmpWO0MPhWrihPQsao4AR2rihPQsdoZfCpWFSegY1VxAjpWFSegZXUuUdhLbVx6N2/ePL700kskyW3btvH0009nJBLhSSedxAULFoSsS07FamfwqVhVnKSOVcVJ6lhVnKSO1c7gU7GqOEkdq4qT1LGqOEktq3M1+ZHR7v/cxx9/jBNOOAGRSCRsylFTsdoZfCpWFSegY1VxAjpWFSegY7Uz+FSsKk5Ax6riBHSsKk5Ay+q+m3nSxTnnnHPOOeecc64B8o10nXPOOeecc8455xogT7o455xzzjnnnHPONUCedHHOOeecc84555xrgDzp4pxzzjnnnHPOOdcAedLFOeecc84555xzrgHypItzzjnnnHPOOedcA+RJF+ecc84555xzzrkGyJMuzjnnnHPOOeeccw3Q/weySEkv9n0pxQAAAABJRU5ErkJggg==\n"},"metadata":{}},{"name":"stdout","text":"  📊 Bottleneck correlation heatmap saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Final Summary ─────────────────────────────────────────\nprint(f\"\"\"\n{'='*60}\n  AUTOENCODER FEATURE SELECTION COMPLETE ✅\n{'='*60}\n \n  Input features   : {INPUT_DIM} (normalized)\n  Bottleneck dim   : {BOTTLENECK_DIM} (compressed)\n  Reduction        : {INPUT_DIM} → {BOTTLENECK_DIM} features  ({(1-BOTTLENECK_DIM/INPUT_DIM)*100:.0f}% smaller)\n  Best Val Loss    : {best_loss:.6f}\n \n  Label: label_multiclass\n    0 = DDoS      {(final_df['label_multiclass']==0).sum():>8,}\n    1 = DoS       {(final_df['label_multiclass']==1).sum():>8,}\n    2 = Normal    {(final_df['label_multiclass']==2).sum():>8,}\n    3 = PortScan  {(final_df['label_multiclass']==3).sum():>8,}\n    4 = SSH       {(final_df['label_multiclass']==4).sum():>8,}\n \n  Output files:\n    📄 AUTOENCODER_FEATURES.csv        ← feed to DL models\n    💾 autoencoder_weights.pth          ← saved model\n    📊 autoencoder_training_curve.png\n    📊 ae_feature_importance.png\n    📊 bottleneck_space.png\n    📊 bottleneck_correlation.png\n \n  → How to use AUTOENCODER_FEATURES.csv:\n     • LogBERT      : input_dim = {BOTTLENECK_DIM}\n     • BiLSTM+Attn  : input_dim = {BOTTLENECK_DIM}\n     • GNN          : node_features = {BOTTLENECK_DIM}\n     (All models receive same 20 bottleneck features)\n{'='*60}\n\"\"\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:11.442023Z","iopub.execute_input":"2026-05-16T06:31:11.442331Z","iopub.status.idle":"2026-05-16T06:31:11.451050Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  AUTOENCODER FEATURE SELECTION COMPLETE ✅\n============================================================\n \n  Input features   : 55 (normalized)\n  Bottleneck dim   : 20 (compressed)\n  Reduction        : 55 → 20 features  (64% smaller)\n  Best Val Loss    : 0.083666\n \n  Label: label_multiclass\n    0 = DDoS       102,071\n    1 = DoS         85,347\n    2 = Normal      18,574\n    3 = PortScan    25,405\n    4 = SSH         17,431\n \n  Output files:\n    📄 AUTOENCODER_FEATURES.csv        ← feed to DL models\n    💾 autoencoder_weights.pth          ← saved model\n    📊 autoencoder_training_curve.png\n    📊 ae_feature_importance.png\n    📊 bottleneck_space.png\n    📊 bottleneck_correlation.png\n \n  → How to use AUTOENCODER_FEATURES.csv:\n     • LogBERT      : input_dim = 20\n     • BiLSTM+Attn  : input_dim = 20\n     • GNN          : node_features = 20\n     (All models receive same 20 bottleneck features)\n============================================================\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Load AE Features Dataset ───────────────────────────────\nDATASET_PATH = '/kaggle/working/AUTOENCODER_FEATURES.csv'\nprint(f\"\\n📂 Loading : {DATASET_PATH}\")\ndf = pd.read_csv(DATASET_PATH)\nprint(f\"✅ Loaded  : {len(df):,} records × {len(df.columns)} columns\")\n\nLABEL        = 'label_multiclass'\nDROP         = [LABEL, 'cloud_platform']\nfeature_cols = [c for c in df.columns if c not in DROP]\nX            = df[feature_cols].fillna(0).values.astype(np.float32)\ny            = df[LABEL].values.astype(np.int64)\nCLASS_NAMES  = ['DDoS', 'DoS', 'Normal', 'PortScan', 'SSH']\nNUM_CLASSES  = len(CLASS_NAMES)\nINPUT_DIM    = X.shape[1]\n\nprint(f\"\\n   Features : {INPUT_DIM}\")\nprint(f\"   Classes  : {CLASS_NAMES}\")\nprint(f\"   Platform : {df['cloud_platform'].value_counts().to_dict()}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:11.451956Z","iopub.execute_input":"2026-05-16T06:31:11.452164Z","iopub.status.idle":"2026-05-16T06:31:11.967327Z"}},"outputs":[{"name":"stdout","text":"\n📂 Loading : /kaggle/working/AUTOENCODER_FEATURES.csv\n✅ Loaded  : 248,828 records × 22 columns\n\n   Features : 20\n   Classes  : ['DDoS', 'DoS', 'Normal', 'PortScan', 'SSH']\n   Platform : {'Azure': 146595, 'AWS': 102233}\n","output_type":"stream"}],"execution_count":null},{"cell_type":"markdown","source":"# **LogBERT**","metadata":{}},{"cell_type":"code","source":"# ── Hyperparameters ────────────────────────────────────────\nD_MODEL    = 64\nN_HEAD     = 4\nN_LAYERS   = 4\nDROPOUT    = 0.1\nBATCH_SIZE = 512\nEPOCHS     = 50\nLR         = 1e-3\nSEQ_LEN    = 4    # 20 features / 4 = 5 features per token\n \nprint(f\"\\n   d_model={D_MODEL} | n_head={N_HEAD} | n_layers={N_LAYERS}\")\nprint(f\"   seq_len={SEQ_LEN} | batch={BATCH_SIZE} | epochs={EPOCHS} | lr={LR}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:11.968449Z","iopub.execute_input":"2026-05-16T06:31:11.968772Z","iopub.status.idle":"2026-05-16T06:31:11.973819Z"}},"outputs":[{"name":"stdout","text":"\n   d_model=64 | n_head=4 | n_layers=4\n   seq_len=4 | batch=512 | epochs=50 | lr=0.001\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Data Preparation ───────────────────────────────────────\nscaler   = StandardScaler()\nX_scaled = scaler.fit_transform(X).astype(np.float32)\n \nX_temp, X_test, y_temp, y_test = train_test_split(\n    X_scaled, y, test_size=0.15, random_state=42, stratify=y)\nX_train, X_val, y_train, y_val = train_test_split(\n    X_temp, y_temp, test_size=0.176, random_state=42, stratify=y_temp)\n \nprint(f\"\\n   Train:{len(X_train):,} | Val:{len(X_val):,} | Test:{len(X_test):,}\")\n \ndef make_loader(X, y, shuffle=True):\n    return DataLoader(\n        TensorDataset(torch.tensor(X, dtype=torch.float32),\n                      torch.tensor(y, dtype=torch.long)),\n        batch_size=BATCH_SIZE, shuffle=shuffle,\n        pin_memory=(DEVICE.type=='cuda'))\n \ntrain_loader = make_loader(X_train, y_train)\nval_loader   = make_loader(X_val,   y_val,   shuffle=False)\ntest_loader  = make_loader(X_test,  y_test,  shuffle=False)\n \nhas_platform = 'cloud_platform' in df.columns\nif has_platform:\n    az = df['cloud_platform']=='Azure'; aw = df['cloud_platform']=='AWS'\n    X_az = scaler.transform(df[feature_cols][az].fillna(0).values.astype(np.float32))\n    X_aw = scaler.transform(df[feature_cols][aw].fillna(0).values.astype(np.float32))\n    y_az = df[LABEL][az].values.astype(np.int64)\n    y_aw = df[LABEL][aw].values.astype(np.int64)\n    print(f\"   Azure:{len(X_az):,} | AWS:{len(X_aw):,}\")\nprint(\"✅ Data ready\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:11.974958Z","iopub.execute_input":"2026-05-16T06:31:11.975745Z","iopub.status.idle":"2026-05-16T06:31:12.356416Z"}},"outputs":[{"name":"stdout","text":"\n   Train:174,278 | Val:37,225 | Test:37,325\n   Azure:146,595 | AWS:102,233\n✅ Data ready\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── LogBERT Model ──────────────────────────────────────────\nclass LogBERT(nn.Module):\n    \"\"\"\n    Transformer-based Log Classifier (LogBERT)\n \n    Splits 20 AE features into SEQ_LEN tokens → applies\n    multi-head self-attention → uses CLS token for classification.\n    \"\"\"\n    def __init__(self, input_dim, num_classes, d_model=64,\n                 nhead=4, num_layers=4, seq_len=4, dropout=0.1):\n        super().__init__()\n        self.seq_len   = seq_len\n        self.tok_dim   = input_dim // seq_len\n \n        # Token projection\n        self.tok_proj  = nn.Linear(self.tok_dim, d_model)\n \n        # CLS token\n        self.cls_token = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n \n        # Learnable positional encoding\n        self.pos_emb   = nn.Parameter(torch.randn(1, seq_len+1, d_model) * 0.01)\n \n        self.dropout   = nn.Dropout(dropout)\n \n        # Transformer encoder\n        enc_layer = nn.TransformerEncoderLayer(\n            d_model=d_model, nhead=nhead,\n            dim_feedforward=d_model*4,\n            dropout=dropout, activation='gelu',\n            batch_first=True, norm_first=True)\n        self.transformer = nn.TransformerEncoder(\n            enc_layer, num_layers=num_layers,\n            norm=nn.LayerNorm(d_model))\n \n        # Classifier head\n        self.head = nn.Sequential(\n            nn.Linear(d_model, d_model//2),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(d_model//2, num_classes))\n \n        # Init weights\n        for m in self.modules():\n            if isinstance(m, nn.Linear):\n                nn.init.xavier_uniform_(m.weight)\n                if m.bias is not None: nn.init.zeros_(m.bias)\n \n    def forward(self, x):\n        B = x.size(0)\n        # Tokenise: (B, seq_len, tok_dim)\n        x = x[:, :self.tok_dim * self.seq_len].view(B, self.seq_len, self.tok_dim)\n        x = self.tok_proj(x)                              # (B, seq_len, d_model)\n \n        # Prepend CLS\n        cls = self.cls_token.expand(B, -1, -1)\n        x   = torch.cat([cls, x], dim=1)                  # (B, seq_len+1, d_model)\n \n        # Add positional embedding + dropout\n        x   = self.dropout(x + self.pos_emb)\n \n        # Transformer\n        x   = self.transformer(x)                         # (B, seq_len+1, d_model)\n \n        # Classify via CLS token\n        return self.head(x[:, 0, :])                      # (B, num_classes)\n \n \nmodel = LogBERT(INPUT_DIM, NUM_CLASSES, D_MODEL, N_HEAD, N_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"\\n  LogBERT ready | Parameters: {n_params:,}\")\nprint(f\"  Flow: {INPUT_DIM}d → {SEQ_LEN} tokens → {N_LAYERS}×Transformer({N_HEAD}h) → {NUM_CLASSES} classes\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:12.360186Z","iopub.execute_input":"2026-05-16T06:31:12.360400Z","iopub.status.idle":"2026-05-16T06:31:12.383018Z"}},"outputs":[{"name":"stdout","text":"\n  LogBERT ready | Parameters: 203,077\n  Flow: 20d → 4 tokens → 4×Transformer(4h) → 5 classes\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Training Setup ─────────────────────────────────────────\n\nmodel    = LogBERT(INPUT_DIM, NUM_CLASSES, D_MODEL, N_HEAD,  \n                   N_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)    \nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"  LogBERT ready | Parameters: {n_params:,}\")\n\ncounts  = np.bincount(y_train, minlength=NUM_CLASSES)\nweights = torch.tensor(1.0/(counts+1e-6), dtype=torch.float32).to(DEVICE)\nweights = weights / weights.sum() * NUM_CLASSES\n\ncriterion = nn.CrossEntropyLoss(weight=weights, label_smoothing=0.05)\noptimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n    optimizer, T_max=EPOCHS, eta_min=1e-5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:12.383956Z","iopub.execute_input":"2026-05-16T06:31:12.384242Z","iopub.status.idle":"2026-05-16T06:31:12.403006Z"}},"outputs":[{"name":"stdout","text":"  LogBERT ready | Parameters: 203,077\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Training Loop ──────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TRAINING LogBERT ({EPOCHS} epochs)\")\nprint(f\"{'='*60}\")\n \nhistory    = {'train_loss':[], 'val_loss':[], 'train_f1':[], 'val_f1':[]}\nbest_f1    = 0.0\nbest_state = None\nt0         = time.time()\n \nfor epoch in range(EPOCHS):\n    # Train\n    model.train()\n    tl, tp, tlab = 0, [], []\n    for bx, by in train_loader:\n        bx, by = bx.to(DEVICE), by.to(DEVICE)\n        out    = model(bx)\n        loss   = criterion(out, by)\n        optimizer.zero_grad(); loss.backward()\n        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n        optimizer.step()          # ← no scheduler.step() here\n        tl += loss.item()\n        tp.extend(out.argmax(1).cpu().numpy())\n        tlab.extend(by.cpu().numpy())\n\n    scheduler.step()\n \n    # Validate\n    model.eval()\n    vl, vp, vlab = 0, [], []\n    with torch.no_grad():\n        for bx, by in val_loader:\n            bx, by = bx.to(DEVICE), by.to(DEVICE)\n            out    = model(bx)\n            vl    += criterion(out, by).item()\n            vp.extend(out.argmax(1).cpu().numpy())\n            vlab.extend(by.cpu().numpy())\n \n    tl_avg = tl/len(train_loader); vl_avg = vl/len(val_loader)\n    tf1    = f1_score(tlab, tp, average='weighted', zero_division=0)\n    vf1    = f1_score(vlab, vp, average='weighted', zero_division=0)\n    vacc   = accuracy_score(vlab, vp)\n \n    history['train_loss'].append(tl_avg); history['val_loss'].append(vl_avg)\n    history['train_f1'].append(tf1);      history['val_f1'].append(vf1)\n \n    if vf1 > best_f1:\n        best_f1    = vf1\n        best_state = {k:v.clone() for k,v in model.state_dict().items()}\n \n    if (epoch+1) % 5 == 0 or epoch == 0:\n        print(f\"  Ep {epoch+1:3d}/{EPOCHS} | \"\n              f\"Loss {tl_avg:.4f}/{vl_avg:.4f} | \"\n              f\"F1 {tf1:.4f}/{vf1:.4f} | \"\n              f\"Acc {vacc:.4f} | {time.time()-t0:.0f}s\")\n \nmodel.load_state_dict(best_state)\nprint(f\"\\n  ★ Best Val F1 : {best_f1:.4f}\")\nprint(f\"  Total time    : {(time.time()-t0)/60:.1f} min\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:31:12.403958Z","iopub.execute_input":"2026-05-16T06:31:12.404287Z","iopub.status.idle":"2026-05-16T06:36:46.852911Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TRAINING LogBERT (50 epochs)\n============================================================\n  Ep   1/50 | Loss 0.4519/0.3822 | F1 0.9682/0.9923 | Acc 0.9924 | 7s\n  Ep   5/50 | Loss 0.3833/0.3741 | F1 0.9912/0.9936 | Acc 0.9937 | 34s\n  Ep  10/50 | Loss 0.3770/0.3691 | F1 0.9917/0.9937 | Acc 0.9938 | 68s\n  Ep  15/50 | Loss 0.3745/0.3677 | F1 0.9921/0.9948 | Acc 0.9948 | 102s\n  Ep  20/50 | Loss 0.3709/0.3629 | F1 0.9923/0.9955 | Acc 0.9956 | 136s\n  Ep  25/50 | Loss 0.3685/0.3611 | F1 0.9926/0.9960 | Acc 0.9960 | 170s\n  Ep  30/50 | Loss 0.3651/0.3617 | F1 0.9932/0.9955 | Acc 0.9955 | 203s\n  Ep  35/50 | Loss 0.3603/0.3545 | F1 0.9943/0.9973 | Acc 0.9973 | 237s\n  Ep  40/50 | Loss 0.3585/0.3501 | F1 0.9956/0.9981 | Acc 0.9981 | 270s\n  Ep  45/50 | Loss 0.3567/0.3497 | F1 0.9963/0.9984 | Acc 0.9984 | 302s\n  Ep  50/50 | Loss 0.3563/0.3498 | F1 0.9966/0.9984 | Acc 0.9984 | 334s\n\n  ★ Best Val F1 : 0.9984\n  Total time    : 5.6 min\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Test Evaluation ────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TEST SET EVALUATION\")\nprint(f\"{'='*60}\")\n \nmodel.eval()\nap, al = [], []\nwith torch.no_grad():\n    for bx, by in test_loader:\n        out = model(bx.to(DEVICE))\n        ap.extend(out.argmax(1).cpu().numpy())\n        al.extend(by.numpy())\n \nacc  = accuracy_score(al, ap)\nprec = precision_score(al, ap, average='weighted', zero_division=0)\nrec  = recall_score(al, ap, average='weighted', zero_division=0)\nf1   = f1_score(al, ap, average='weighted', zero_division=0)\nrep  = classification_report(al, ap, target_names=CLASS_NAMES,\n                              digits=4, output_dict=True)\n \nprint(f\"\\n  Accuracy  : {acc:.4f}\")\nprint(f\"  Precision : {prec:.4f}\")\nprint(f\"  Recall    : {rec:.4f}\")\nprint(f\"  F1-Score  : {f1:.4f}\")\nprint(f\"\\n{classification_report(al, ap, target_names=CLASS_NAMES, digits=4)}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:36:46.853706Z","iopub.execute_input":"2026-05-16T06:36:46.853913Z","iopub.status.idle":"2026-05-16T06:36:47.470457Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TEST SET EVALUATION\n============================================================\n\n  Accuracy  : 0.9979\n  Precision : 0.9979\n  Recall    : 0.9979\n  F1-Score  : 0.9979\n\n              precision    recall  f1-score   support\n\n        DDoS     0.9973    0.9987    0.9980     15311\n         DoS     0.9989    1.0000    0.9995     12802\n      Normal     0.9960    0.9817    0.9888      2786\n    PortScan     0.9963    0.9976    0.9970      3811\n         SSH     1.0000    1.0000    1.0000      2615\n\n    accuracy                         0.9979     37325\n   macro avg     0.9977    0.9956    0.9966     37325\nweighted avg     0.9979    0.9979    0.9979     37325\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Cross-Platform Validation ─────────────────────────────\ncross_results = {}\nif has_platform:\n    print(f\"\\n{'='*60}\")\n    print(f\"  CROSS-PLATFORM VALIDATION (Thesis H2)\")\n    print(f\"{'='*60}\")\n    for tr_name, Xtr, ytr, te_name, Xte, yte in [\n        ('Azure', X_az, y_az, 'AWS',   X_aw, y_aw),\n        ('AWS',   X_aw, y_aw, 'Azure', X_az, y_az)]:\n \n        m2   = LogBERT(INPUT_DIM, NUM_CLASSES, D_MODEL, N_HEAD, N_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)\n        opt2 = torch.optim.AdamW(m2.parameters(), lr=LR, weight_decay=1e-4)\n        cr2  = nn.CrossEntropyLoss(label_smoothing=0.05)\n        dl2  = make_loader(Xtr, ytr); te2 = make_loader(Xte, yte, shuffle=False)\n \n        for ep in range(EPOCHS):\n            m2.train()\n            for bx, by in dl2:\n                bx, by = bx.to(DEVICE), by.to(DEVICE)\n                l = cr2(m2(bx), by); opt2.zero_grad(); l.backward(); opt2.step()\n \n        m2.eval(); cp, cl = [], []\n        with torch.no_grad():\n            for bx, by in te2:\n                cp.extend(m2(bx.to(DEVICE)).argmax(1).cpu().numpy())\n                cl.extend(by.numpy())\n        cf1 = f1_score(cl, cp, average='weighted', zero_division=0)\n        cac = accuracy_score(cl, cp)\n        key = f\"{tr_name}→{te_name}\"\n        cross_results[key] = {'f1': round(cf1,4), 'accuracy': round(cac,4)}\n        print(f\"\\n  {key}: Acc={cac:.4f}  F1={cf1:.4f}\")\n        print(classification_report(cl, cp, target_names=CLASS_NAMES, digits=4))\n \n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:36:47.471444Z","iopub.execute_input":"2026-05-16T06:36:47.471793Z","iopub.status.idle":"2026-05-16T06:43:14.526153Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  CROSS-PLATFORM VALIDATION (Thesis H2)\n============================================================\n\n  Azure→AWS: Acc=0.0550  F1=0.0473\n              precision    recall  f1-score   support\n\n        DDoS     0.5179    0.1713    0.2574     17413\n         DoS     0.0000    0.0000    0.0000     69664\n      Normal     0.0441    0.6234    0.0824      4238\n    PortScan     0.0000    0.0000    0.0000     10800\n         SSH     0.0000    0.0000    0.0000       118\n\n    accuracy                         0.0550    102233\n   macro avg     0.1124    0.1589    0.0679    102233\nweighted avg     0.0900    0.0550    0.0473    102233\n\n\n  AWS→Azure: Acc=0.1994  F1=0.0783\n              precision    recall  f1-score   support\n\n        DDoS     0.4615    0.0001    0.0001     84658\n         DoS     0.1434    1.0000    0.2508     15683\n      Normal     0.3641    0.9443    0.5256     14336\n    PortScan     0.0000    0.0000    0.0000     14605\n         SSH     0.0000    0.0000    0.0000     17313\n\n    accuracy                         0.1994    146595\n   macro avg     0.1938    0.3889    0.1553    146595\nweighted avg     0.3175    0.1994    0.0783    146595\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Plots ─────────────────────────────────────────────────\n# Training curves\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\nax1.plot(history['train_loss'], color='#e74c3c', lw=2, label='Train')\nax1.plot(history['val_loss'],   color='#3498db', lw=2, label='Val')\nax1.set(title='LogBERT Loss', xlabel='Epoch', ylabel='Loss')\nax1.legend(); ax1.grid(alpha=0.3)\n \nax2.plot(history['train_f1'], color='#e74c3c', lw=2, label='Train')\nax2.plot(history['val_f1'],   color='#3498db', lw=2, label='Val')\nax2.set(title='LogBERT F1-Score', xlabel='Epoch', ylabel='F1 (weighted)')\nax2.legend(); ax2.grid(alpha=0.3)\nplt.suptitle('LogBERT Training Progress', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/logbert_training_curves.png', dpi=150)\nplt.show()\n \n# Confusion matrix\ncm    = confusion_matrix(al, ap)\ncm_pct = cm.astype(float)/cm.sum(axis=1,keepdims=True)*100\nfig, (ax1,ax2) = plt.subplots(1, 2, figsize=(16, 6))\nsns.heatmap(cm,     annot=True, fmt='d',   cmap='Blues', ax=ax1,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax1.set(title='Confusion Matrix (Counts)', ylabel='True', xlabel='Predicted')\nsns.heatmap(cm_pct, annot=True, fmt='.1f', cmap='Blues', ax=ax2,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax2.set(title='Confusion Matrix (%)', ylabel='True', xlabel='Predicted')\nplt.suptitle(f'LogBERT | F1={f1:.4f}  Acc={acc:.4f}', fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/logbert_confusion_matrix.png', dpi=150)\nplt.show()\n \n# Per-class bar chart\nmet_df = pd.DataFrame({c:[rep[c]['precision'],rep[c]['recall'],rep[c]['f1-score']]\n                        for c in CLASS_NAMES}, index=['Precision','Recall','F1'])\nfig, ax = plt.subplots(figsize=(12,5))\nmet_df.T.plot(kind='bar', ax=ax, color=['#3498db','#2ecc71','#e74c3c'], edgecolor='white')\nax.set(title='LogBERT Per-Class Performance', xlabel='Attack Class', ylabel='Score')\nax.set_ylim(0, 1.15); ax.legend(loc='lower right')\nax.set_xticklabels(CLASS_NAMES, rotation=0)\nfor c in ax.containers: ax.bar_label(c, fmt='%.3f', fontsize=8, padding=2)\nplt.tight_layout()\nplt.savefig('/kaggle/working/logbert_per_class.png', dpi=150)\nplt.show()\nprint(\"📊 All plots saved\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:14.527440Z","iopub.execute_input":"2026-05-16T06:43:14.527786Z","iopub.status.idle":"2026-05-16T06:43:16.395547Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x500 with 2 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phQAAAAAAAAsRygFAAAAAAAAyxFKAQAAAAAAwHKEUgAAAAAAALAcoRQAAAAAAAAsRygFAAAAAAAAyxFKAQAAAAAAwHKEUgAAAAAAALAcoRQAAAAAAAAsRygFAAAAAAAAyxFKAQAAAAAAwHKEUgAAAAAAALDc/wFTCzK527LgXAAAAABJRU5ErkJggg==\n"},"metadata":{}},{"name":"stdout","text":"📊 All plots saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Save Results ───────────────────────────────────────────\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n\nresults = {\n    'model': 'LogBERT',\n    'architecture': {\n        'input_dim':  INPUT_DIM,\n        'd_model':    D_MODEL,\n        'n_head':     N_HEAD,\n        'n_layers':   N_LAYERS,\n        'seq_len':    SEQ_LEN,\n        'parameters': n_params,\n    },\n    'test_results': {\n        'accuracy':  round(acc,  4),\n        'precision': round(prec, 4),\n        'recall':    round(rec,  4),\n        'f1_score':  round(f1,   4),\n    },\n    'best_val_f1':    round(best_f1, 4),\n    'epochs':         EPOCHS,\n    'cross_platform': cross_results,\n    'per_class': {\n        c: {\n            'precision': round(rep[c]['precision'], 4),\n            'recall':    round(rep[c]['recall'],    4),\n            'f1':        round(rep[c]['f1-score'],  4),\n        } for c in CLASS_NAMES\n    },\n    'roc_auc': all_aucs,   # all_aucs already defined in ROC cell above\n}\n\nwith open('/kaggle/working/logbert_results.json', 'w') as f:\n    json.dump(results, f, indent=2)\n\ntorch.save(model.state_dict(), '/kaggle/working/logbert_weights.pth')\n\nprint(f\"\"\"\n{'='*55}\n  LogBERT COMPLETE ✅\n{'='*55}\n  Accuracy  : {acc:.4f}\n  Precision : {prec:.4f}\n  Recall    : {rec:.4f}\n  F1-Score  : {f1:.4f}\n\n  ROC-AUC:\n    DDoS          : {all_aucs.get('DDoS', 0):.4f}\n    DoS           : {all_aucs.get('DoS', 0):.4f}\n    Normal        : {all_aucs.get('Normal', 0):.4f}\n    PortScan      : {all_aucs.get('PortScan', 0):.4f}\n    SSH           : {all_aucs.get('SSH', 0):.4f}\n    Macro-Average : {all_aucs.get('Macro-Average', 0):.4f}\n\n  Cross-Platform:\n    {cross_results}\n\n  Saved:\n    📄 logbert_results.json\n    💾 logbert_weights.pth\n{'='*55}\n\"\"\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:16.396791Z","iopub.execute_input":"2026-05-16T06:43:16.397770Z","iopub.status.idle":"2026-05-16T06:43:16.415237Z"}},"outputs":[{"name":"stdout","text":"\n=======================================================\n  LogBERT COMPLETE ✅\n=======================================================\n  Accuracy  : 0.9979\n  Precision : 0.9979\n  Recall    : 0.9979\n  F1-Score  : 0.9979\n\n  ROC-AUC:\n    DDoS          : 0.9999\n    DoS           : 1.0000\n    Normal        : 0.9998\n    PortScan      : 0.9998\n    SSH           : 1.0000\n    Macro-Average : 0.9997\n\n  Cross-Platform:\n    {'Azure→AWS': {'f1': 0.0473, 'accuracy': 0.055}, 'AWS→Azure': {'f1': 0.0783, 'accuracy': 0.1994}}\n\n  Saved:\n    📄 logbert_results.json\n    💾 logbert_weights.pth\n=======================================================\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── ROC CURVE (One-vs-Rest for each class) ────────────────────────\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn.metrics import roc_curve, auc\n\n# Get predicted probabilities on test set\nmodel.eval()\nall_probs, all_labels_roc = [], []\nwith torch.no_grad():\n    for bx, by in test_loader:\n        out   = model(bx.to(DEVICE))\n        probs = F.softmax(out, dim=1)\n        all_probs.extend(probs.cpu().numpy())\n        all_labels_roc.extend(by.numpy())\n\nall_probs  = np.array(all_probs)           # (N, 5)\nall_labels_roc = np.array(all_labels_roc)  # (N,)\n\n# Binarize labels for One-vs-Rest ROC\ny_bin = label_binarize(all_labels_roc, classes=[0, 1, 2, 3, 4])\n\n# Colors per class\ncolors = ['#e74c3c', '#e67e22', '#2ecc71', '#3498db', '#9b59b6']\n\n# ── Plot 1: All classes on one figure ─────────────────────────────\nfig, ax = plt.subplots(figsize=(10, 8))\n\nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc     = auc(fpr, tpr)\n    ax.plot(fpr, tpr, color=color, lw=2,\n            label=f'{cls_name}  (AUC = {roc_auc:.4f})')\n\nax.plot([0, 1], [0, 1], 'k--', lw=1, label='Random Classifier')\nax.fill_between([0, 1], [0, 1], alpha=0.05, color='gray')\nax.set_xlim([0.0, 1.0])\nax.set_ylim([0.0, 1.05])\nax.set_xlabel('False Positive Rate', fontsize=13)\nax.set_ylabel('True Positive Rate', fontsize=13)\nax.set_title('LogBERT - ROC Curves (One-vs-Rest)', fontsize=14, fontweight='bold')\nax.legend(loc='lower right', fontsize=11)\nax.grid(alpha=0.3)\nplt.tight_layout()\nplt.savefig('/kaggle/working/logbert_roc_curves.png', dpi=150)\nplt.show()\n\n# ── Plot 2: Individual subplot per class ──────────────────────────\nfig, axes = plt.subplots(2, 3, figsize=(16, 10))\naxes = axes.flatten()\n\nall_aucs = {}\nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, thresholds = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc = auc(fpr, tpr)\n    all_aucs[cls_name] = round(roc_auc, 4)\n\n    ax = axes[i]\n    ax.plot(fpr, tpr, color=color, lw=2.5,\n            label=f'AUC = {roc_auc:.4f}')\n    ax.fill_between(fpr, tpr, alpha=0.15, color=color)\n    ax.plot([0, 1], [0, 1], 'k--', lw=1)\n    ax.set_xlim([0.0, 1.0])\n    ax.set_ylim([0.0, 1.05])\n    ax.set_xlabel('False Positive Rate')\n    ax.set_ylabel('True Positive Rate')\n    ax.set_title(f'{cls_name}', fontsize=13, fontweight='bold')\n    ax.legend(loc='lower right')\n    ax.grid(alpha=0.3)\n\n# Macro-average ROC\nfpr_all = np.linspace(0, 1, 200)\ntpr_all = np.zeros_like(fpr_all)\nfor i in range(NUM_CLASSES):\n    fpr_i, tpr_i, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    tpr_all += np.interp(fpr_all, fpr_i, tpr_i)\ntpr_all /= NUM_CLASSES\nmacro_auc = auc(fpr_all, tpr_all)\nall_aucs['Macro-Average'] = round(macro_auc, 4)\n\naxes[5].plot(fpr_all, tpr_all, color='black', lw=2.5,\n             label=f'Macro AUC = {macro_auc:.4f}')\naxes[5].fill_between(fpr_all, tpr_all, alpha=0.1, color='black')\naxes[5].plot([0, 1], [0, 1], 'k--', lw=1)\naxes[5].set_xlim([0.0, 1.0])\naxes[5].set_ylim([0.0, 1.05])\naxes[5].set_xlabel('False Positive Rate')\naxes[5].set_ylabel('True Positive Rate')\naxes[5].set_title('Macro-Average', fontsize=13, fontweight='bold')\naxes[5].legend(loc='lower right')\naxes[5].grid(alpha=0.3)\n\nplt.suptitle('LogBERT - ROC Curves Per Class', fontsize=15, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/logbert_roc_per_class.png', dpi=150)\nplt.show()\n\n# ── Print AUC summary ──────────────────────────────────────────────\nprint(f\"\\n  ROC-AUC Summary:\")\nprint(f\"  {'─'*35}\")\nfor cls_name, auc_val in all_aucs.items():\n    quality = \"✅ Excellent\" if auc_val >= 0.99 else \\\n              \"✅ Very Good\" if auc_val >= 0.95 else \\\n              \"⚠ Good\"      if auc_val >= 0.90 else \"❌ Needs work\"\n    print(f\"    {cls_name:15s}: AUC = {auc_val:.4f}  {quality}\")\n\nprint(f\"\\n  Saved:\")\nprint(f\"    📊 logbert_roc_curves.png     (all classes combined)\")\nprint(f\"    📊 logbert_roc_per_class.png  (individual + macro)\")\n\n# Add AUC to results JSON\nresults['roc_auc'] = all_aucs\nwith open('/kaggle/working/logbert_results.json', 'w') as f:\n    json.dump(results, f, indent=2)\nprint(f\"    📄 logbert_results.json updated with AUC scores\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:16.416099Z","iopub.execute_input":"2026-05-16T06:43:16.416416Z","iopub.status.idle":"2026-05-16T06:43:19.297593Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}},{"name":"stdout","text":"\n  ROC-AUC Summary:\n  ───────────────────────────────────\n    DDoS           : AUC = 0.9997  ✅ Excellent\n    DoS            : AUC = 1.0000  ✅ Excellent\n    Normal         : AUC = 0.9992  ✅ Excellent\n    PortScan       : AUC = 0.9998  ✅ Excellent\n    SSH            : AUC = 1.0000  ✅ Excellent\n    Macro-Average  : AUC = 0.9994  ✅ Excellent\n\n  Saved:\n    📊 logbert_roc_curves.png     (all classes combined)\n    📊 logbert_roc_per_class.png  (individual + macro)\n    📄 logbert_results.json updated with AUC scores\n","output_type":"stream"}],"execution_count":null},{"cell_type":"markdown","source":"# **BiLSTM + Attention**","metadata":{}},{"cell_type":"code","source":" # ── Hyperparameters ────────────────────────────────────────\nHIDDEN_DIM = 128      # BiLSTM hidden units per direction\nNUM_LAYERS = 2        # BiLSTM stacked layers\nDROPOUT    = 0.3\nBATCH_SIZE = 512\nEPOCHS     = 50\nLR         = 1e-3\nSEQ_LEN    = 4        # Split 20 features into 4 time steps\n \nprint(f\"\\n   hidden_dim={HIDDEN_DIM} | num_layers={NUM_LAYERS}\")\nprint(f\"   seq_len={SEQ_LEN} | batch={BATCH_SIZE} | epochs={EPOCHS} | lr={LR}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:19.298557Z","iopub.execute_input":"2026-05-16T06:43:19.298854Z","iopub.status.idle":"2026-05-16T06:43:19.304683Z"}},"outputs":[{"name":"stdout","text":"\n   hidden_dim=128 | num_layers=2\n   seq_len=4 | batch=512 | epochs=50 | lr=0.001\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Data Preparation ───────────────────────────────────────\nscaler   = StandardScaler()\nX_scaled = scaler.fit_transform(X).astype(np.float32)\n \nX_temp, X_test, y_temp, y_test = train_test_split(\n    X_scaled, y, test_size=0.15, random_state=42, stratify=y)\nX_train, X_val, y_train, y_val = train_test_split(\n    X_temp, y_temp, test_size=0.176, random_state=42, stratify=y_temp)\n \nprint(f\"\\n   Train:{len(X_train):,} | Val:{len(X_val):,} | Test:{len(X_test):,}\")\n \ndef make_loader(X, y, shuffle=True):\n    return DataLoader(\n        TensorDataset(torch.tensor(X, dtype=torch.float32),\n                      torch.tensor(y, dtype=torch.long)),\n        batch_size=BATCH_SIZE, shuffle=shuffle,\n        pin_memory=(DEVICE.type == 'cuda'))\n \ntrain_loader = make_loader(X_train, y_train)\nval_loader   = make_loader(X_val,   y_val,   shuffle=False)\ntest_loader  = make_loader(X_test,  y_test,  shuffle=False)\n \nhas_platform = 'cloud_platform' in df.columns\nif has_platform:\n    az = df['cloud_platform'] == 'Azure'\n    aw = df['cloud_platform'] == 'AWS'\n    X_az = scaler.transform(df[feature_cols][az].fillna(0).values.astype(np.float32))\n    X_aw = scaler.transform(df[feature_cols][aw].fillna(0).values.astype(np.float32))\n    y_az = df[LABEL][az].values.astype(np.int64)\n    y_aw = df[LABEL][aw].values.astype(np.int64)\n    print(f\"   Azure:{len(X_az):,} | AWS:{len(X_aw):,}\")\nprint(\"✅ Data ready\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:19.305589Z","iopub.execute_input":"2026-05-16T06:43:19.306082Z","iopub.status.idle":"2026-05-16T06:43:19.657344Z"}},"outputs":[{"name":"stdout","text":"\n   Train:174,278 | Val:37,225 | Test:37,325\n   Azure:146,595 | AWS:102,233\n✅ Data ready\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── BiLSTM + Attention Model ──────────────────────────────\nclass BahdanauAttention(nn.Module):\n    \"\"\"\n    Bahdanau (Additive) Attention Mechanism.\n \n    Learns a weight for each time step of the BiLSTM output.\n    Weighted sum = context vector capturing the most relevant\n    temporal patterns for classification.\n    \"\"\"\n    def __init__(self, hidden_dim):\n        super().__init__()\n        # hidden_dim*2 because BiLSTM is bidirectional\n        self.attn  = nn.Linear(hidden_dim * 2, hidden_dim)\n        self.v     = nn.Linear(hidden_dim, 1, bias=False)\n \n    def forward(self, lstm_out):\n        # lstm_out : (B, seq_len, hidden_dim*2)\n        energy     = torch.tanh(self.attn(lstm_out))  # (B, seq_len, hidden_dim)\n        scores     = self.v(energy)                    # (B, seq_len, 1)\n        weights    = F.softmax(scores, dim=1)          # (B, seq_len, 1)\n        context    = (weights * lstm_out).sum(dim=1)   # (B, hidden_dim*2)\n        return context, weights.squeeze(-1)            # context, attn_weights\n \n \nclass BiLSTMAttention(nn.Module):\n    \"\"\"\n    BiLSTM + Bahdanau Attention for forensic log classification.\n \n    Treats 20 AE features as a time series of SEQ_LEN steps.\n    BiLSTM captures forward + backward temporal dependencies.\n    Attention focuses on the most discriminative time steps.\n \n    Input  : (B, INPUT_DIM=20)\n    Output : (B, NUM_CLASSES=5)\n    \"\"\"\n    def __init__(self, input_dim, num_classes, hidden_dim=128,\n                 num_layers=2, seq_len=4, dropout=0.3):\n        super().__init__()\n        self.seq_len  = seq_len\n        self.tok_dim  = input_dim // seq_len\n \n        # Input projection: tok_dim → hidden_dim\n        self.input_proj = nn.Sequential(\n            nn.Linear(self.tok_dim, hidden_dim),\n            nn.LayerNorm(hidden_dim),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n        )\n \n        # Bidirectional LSTM\n        self.lstm = nn.LSTM(\n            input_size=hidden_dim,\n            hidden_size=hidden_dim,\n            num_layers=num_layers,\n            bidirectional=True,\n            batch_first=True,\n            dropout=dropout if num_layers > 1 else 0.0,\n        )\n \n        # Bahdanau attention\n        self.attention = BahdanauAttention(hidden_dim)\n \n        # Classifier head\n        self.classifier = nn.Sequential(\n            nn.Linear(hidden_dim * 2, hidden_dim),\n            nn.LayerNorm(hidden_dim),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_dim, hidden_dim // 2),\n            nn.ReLU(),\n            nn.Dropout(dropout / 2),\n            nn.Linear(hidden_dim // 2, num_classes),\n        )\n \n        # Weight init\n        for name, param in self.lstm.named_parameters():\n            if 'weight_ih' in name: nn.init.xavier_uniform_(param)\n            elif 'weight_hh' in name: nn.init.orthogonal_(param)\n            elif 'bias' in name: nn.init.zeros_(param)\n        for m in self.classifier.modules():\n            if isinstance(m, nn.Linear):\n                nn.init.xavier_uniform_(m.weight)\n                if m.bias is not None: nn.init.zeros_(m.bias)\n \n    def forward(self, x):\n        B = x.size(0)\n \n        # Split into time steps : (B, seq_len, tok_dim)\n        x = x[:, :self.tok_dim * self.seq_len].view(B, self.seq_len, self.tok_dim)\n \n        # Project each token : (B, seq_len, hidden_dim)\n        x = self.input_proj(x)\n \n        # BiLSTM : (B, seq_len, hidden_dim*2)\n        lstm_out, _ = self.lstm(x)\n \n        # Attention : context (B, hidden_dim*2), weights (B, seq_len)\n        context, attn_weights = self.attention(lstm_out)\n \n        # Classify\n        return self.classifier(context)\n \n    def get_attention_weights(self, x):\n        \"\"\"Return attention weights for visualisation\"\"\"\n        B = x.size(0)\n        x = x[:, :self.tok_dim * self.seq_len].view(B, self.seq_len, self.tok_dim)\n        x = self.input_proj(x)\n        lstm_out, _ = self.lstm(x)\n        _, attn_weights = self.attention(lstm_out)\n        return attn_weights\n \n \nmodel    = BiLSTMAttention(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM,\n                            NUM_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"\\n  BiLSTM+Attention ready | Parameters: {n_params:,}\")\nprint(f\"  Flow: {INPUT_DIM}d → {SEQ_LEN} steps → BiLSTM({HIDDEN_DIM}×2) → Attention → {NUM_CLASSES} classes\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:19.658473Z","iopub.execute_input":"2026-05-16T06:43:19.658796Z","iopub.status.idle":"2026-05-16T06:43:19.795206Z"}},"outputs":[{"name":"stdout","text":"\n  BiLSTM+Attention ready | Parameters: 735,237\n  Flow: 20d → 4 steps → BiLSTM(128×2) → Attention → 5 classes\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Training Setup ─────────────────────────────────────────\n\nmodel    = BiLSTMAttention(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM,\n                           NUM_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"  BiLSTM+Attention ready | Parameters: {n_params:,}\")\n\ncounts  = np.bincount(y_train, minlength=NUM_CLASSES)\nweights = torch.tensor(1.0/(counts+1e-6), dtype=torch.float32).to(DEVICE)\nweights = weights / weights.sum() * NUM_CLASSES\n\ncriterion = nn.CrossEntropyLoss(weight=weights, label_smoothing=0.05)\noptimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n    optimizer, T_max=EPOCHS, eta_min=1e-5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:19.796305Z","iopub.execute_input":"2026-05-16T06:43:19.796668Z","iopub.status.idle":"2026-05-16T06:43:19.832529Z"}},"outputs":[{"name":"stdout","text":"  BiLSTM+Attention ready | Parameters: 735,237\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Training Loop ──────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TRAINING BiLSTM+Attention ({EPOCHS} epochs)\")\nprint(f\"{'='*60}\")\n \nhistory    = {'train_loss':[], 'val_loss':[], 'train_f1':[], 'val_f1':[]}\nbest_f1    = 0.0\nbest_state = None\nt0         = time.time()\n \nfor epoch in range(EPOCHS):\n    # Train\n    model.train()\n    tl, tp, tlab = 0, [], []\n    for bx, by in train_loader:\n        bx, by = bx.to(DEVICE), by.to(DEVICE)\n        out    = model(bx)\n        loss   = criterion(out, by)\n        optimizer.zero_grad(); loss.backward()\n        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n        optimizer.step()\n        tl += loss.item()\n        tp.extend(out.argmax(1).cpu().numpy())\n        tlab.extend(by.cpu().numpy())\n \n    scheduler.step()\n \n    # Validate\n    model.eval()\n    vl, vp, vlab = 0, [], []\n    with torch.no_grad():\n        for bx, by in val_loader:\n            bx, by = bx.to(DEVICE), by.to(DEVICE)\n            out    = model(bx)\n            vl    += criterion(out, by).item()\n            vp.extend(out.argmax(1).cpu().numpy())\n            vlab.extend(by.cpu().numpy())\n \n    tl_avg = tl / len(train_loader)\n    vl_avg = vl / len(val_loader)\n    tf1    = f1_score(tlab, tp,  average='weighted', zero_division=0)\n    vf1    = f1_score(vlab, vp,  average='weighted', zero_division=0)\n    vacc   = accuracy_score(vlab, vp)\n \n    history['train_loss'].append(tl_avg); history['val_loss'].append(vl_avg)\n    history['train_f1'].append(tf1);      history['val_f1'].append(vf1)\n \n    if vf1 > best_f1:\n        best_f1    = vf1\n        best_state = {k: v.clone() for k, v in model.state_dict().items()}\n \n    if (epoch + 1) % 5 == 0 or epoch == 0:\n        print(f\"  Ep {epoch+1:3d}/{EPOCHS} | \"\n              f\"Loss {tl_avg:.4f}/{vl_avg:.4f} | \"\n              f\"F1 {tf1:.4f}/{vf1:.4f} | \"\n              f\"Acc {vacc:.4f} | {time.time()-t0:.0f}s\")\n \nmodel.load_state_dict(best_state)\nprint(f\"\\n  ★ Best Val F1 : {best_f1:.4f}\")\nprint(f\"  Total time    : {(time.time()-t0)/60:.1f} min\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:43:19.833460Z","iopub.execute_input":"2026-05-16T06:43:19.833719Z","iopub.status.idle":"2026-05-16T06:48:36.558812Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TRAINING BiLSTM+Attention (50 epochs)\n============================================================\n  Ep   1/50 | Loss 0.4896/0.3975 | F1 0.9538/0.9848 | Acc 0.9848 | 7s\n  Ep   5/50 | Loss 0.3882/0.3756 | F1 0.9913/0.9924 | Acc 0.9924 | 31s\n  Ep  10/50 | Loss 0.3788/0.3696 | F1 0.9923/0.9932 | Acc 0.9932 | 64s\n  Ep  15/50 | Loss 0.3739/0.3653 | F1 0.9926/0.9948 | Acc 0.9948 | 95s\n  Ep  20/50 | Loss 0.3707/0.3622 | F1 0.9927/0.9948 | Acc 0.9949 | 127s\n  Ep  25/50 | Loss 0.3643/0.3545 | F1 0.9929/0.9961 | Acc 0.9961 | 159s\n  Ep  30/50 | Loss 0.3599/0.3496 | F1 0.9946/0.9982 | Acc 0.9982 | 190s\n  Ep  35/50 | Loss 0.3569/0.3503 | F1 0.9958/0.9981 | Acc 0.9981 | 222s\n  Ep  40/50 | Loss 0.3546/0.3467 | F1 0.9967/0.9988 | Acc 0.9988 | 254s\n  Ep  45/50 | Loss 0.3537/0.3464 | F1 0.9971/0.9988 | Acc 0.9988 | 286s\n  Ep  50/50 | Loss 0.3532/0.3464 | F1 0.9973/0.9989 | Acc 0.9989 | 317s\n\n  ★ Best Val F1 : 0.9990\n  Total time    : 5.3 min\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Test Evaluation ────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TEST SET EVALUATION\")\nprint(f\"{'='*60}\")\n \nmodel.eval()\nap, al, all_probs = [], [], []\nwith torch.no_grad():\n    for bx, by in test_loader:\n        out   = model(bx.to(DEVICE))\n        probs = F.softmax(out, dim=1)\n        ap.extend(out.argmax(1).cpu().numpy())\n        al.extend(by.numpy())\n        all_probs.extend(probs.cpu().numpy())\n \nall_probs = np.array(all_probs)\nacc  = accuracy_score(al, ap)\nprec = precision_score(al, ap, average='weighted', zero_division=0)\nrec  = recall_score(al, ap,    average='weighted', zero_division=0)\nf1   = f1_score(al, ap,        average='weighted', zero_division=0)\nrep  = classification_report(al, ap, target_names=CLASS_NAMES,\n                              digits=4, output_dict=True)\n \nprint(f\"\\n  Accuracy  : {acc:.4f}\")\nprint(f\"  Precision : {prec:.4f}\")\nprint(f\"  Recall    : {rec:.4f}\")\nprint(f\"  F1-Score  : {f1:.4f}\")\nprint(f\"\\n{classification_report(al, ap, target_names=CLASS_NAMES, digits=4)}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:48:36.559838Z","iopub.execute_input":"2026-05-16T06:48:36.560182Z","iopub.status.idle":"2026-05-16T06:48:37.500096Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TEST SET EVALUATION\n============================================================\n\n  Accuracy  : 0.9989\n  Precision : 0.9989\n  Recall    : 0.9989\n  F1-Score  : 0.9989\n\n              precision    recall  f1-score   support\n\n        DDoS     0.9992    0.9992    0.9992     15311\n         DoS     0.9991    1.0000    0.9995     12802\n      Normal     0.9975    0.9928    0.9951      2786\n    PortScan     0.9974    0.9979    0.9976      3811\n         SSH     1.0000    1.0000    1.0000      2615\n\n    accuracy                         0.9989     37325\n   macro avg     0.9986    0.9980    0.9983     37325\nweighted avg     0.9989    0.9989    0.9989     37325\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Cross-Platform Validation (H2) ────────────────────────\ncross_results = {}\nif has_platform:\n    print(f\"\\n{'='*60}\")\n    print(f\"  CROSS-PLATFORM VALIDATION (Thesis H2)\")\n    print(f\"{'='*60}\")\n    for tr_name, Xtr, ytr, te_name, Xte, yte in [\n        ('Azure', X_az, y_az, 'AWS',   X_aw, y_aw),\n        ('AWS',   X_aw, y_aw, 'Azure', X_az, y_az)]:\n \n        m2  = BiLSTMAttention(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM,\n                               NUM_LAYERS, SEQ_LEN, DROPOUT).to(DEVICE)\n        o2  = torch.optim.AdamW(m2.parameters(), lr=LR, weight_decay=1e-4)\n        s2  = torch.optim.lr_scheduler.CosineAnnealingLR(o2, T_max=EPOCHS, eta_min=1e-5)\n        cr2 = nn.CrossEntropyLoss(label_smoothing=0.05)\n        dl2 = make_loader(Xtr, ytr)\n        te2 = make_loader(Xte, yte, shuffle=False)\n \n        for ep in range(EPOCHS):\n            m2.train()\n            for bx, by in dl2:\n                bx, by = bx.to(DEVICE), by.to(DEVICE)\n                l = cr2(m2(bx), by)\n                o2.zero_grad(); l.backward()\n                torch.nn.utils.clip_grad_norm_(m2.parameters(), 1.0)\n                o2.step()\n            s2.step()\n \n        m2.eval(); cp, cl = [], []\n        with torch.no_grad():\n            for bx, by in te2:\n                cp.extend(m2(bx.to(DEVICE)).argmax(1).cpu().numpy())\n                cl.extend(by.numpy())\n \n        cf1 = f1_score(cl, cp, average='weighted', zero_division=0)\n        cac = accuracy_score(cl, cp)\n        key = f\"{tr_name}→{te_name}\"\n        cross_results[key] = {'f1': round(cf1, 4), 'accuracy': round(cac, 4)}\n        print(f\"\\n  {key}: Acc={cac:.4f}  F1={cf1:.4f}\")\n        print(classification_report(cl, cp, target_names=CLASS_NAMES, digits=4))\n ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:48:37.501231Z","iopub.execute_input":"2026-05-16T06:48:37.501706Z","iopub.status.idle":"2026-05-16T06:53:22.732432Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  CROSS-PLATFORM VALIDATION (Thesis H2)\n============================================================\n\n  Azure→AWS: Acc=0.1169  F1=0.0807\n              precision    recall  f1-score   support\n\n        DDoS     0.4351    0.4626    0.4484     17413\n         DoS     0.0000    0.0000    0.0000     69664\n      Normal     0.0550    0.9193    0.1038      4238\n    PortScan     0.0002    0.0002    0.0002     10800\n         SSH     0.0000    0.0000    0.0000       118\n\n    accuracy                         0.1169    102233\n   macro avg     0.0981    0.2764    0.1105    102233\nweighted avg     0.0764    0.1169    0.0807    102233\n\n\n  AWS→Azure: Acc=0.1983  F1=0.1030\n              precision    recall  f1-score   support\n\n        DDoS     0.0032    0.0001    0.0001     84658\n         DoS     0.6366    1.0000    0.7780     15683\n      Normal     0.1116    0.9318    0.1993     14336\n    PortScan     0.2462    0.0011    0.0022     14605\n         SSH     0.0000    0.0000    0.0000     17313\n\n    accuracy                         0.1983    146595\n   macro avg     0.1995    0.3866    0.1959    146595\nweighted avg     0.1054    0.1983    0.1030    146595\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Plots ─────────────────────────────────────────────────\n# 1. Training curves\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\nax1.plot(history['train_loss'], color='#e74c3c', lw=2, label='Train')\nax1.plot(history['val_loss'],   color='#3498db', lw=2, label='Val')\nax1.set(title='BiLSTM+Attention Loss', xlabel='Epoch', ylabel='Loss')\nax1.legend(); ax1.grid(alpha=0.3)\n \nax2.plot(history['train_f1'], color='#e74c3c', lw=2, label='Train')\nax2.plot(history['val_f1'],   color='#3498db', lw=2, label='Val')\nax2.set(title='BiLSTM+Attention F1-Score', xlabel='Epoch', ylabel='F1 (weighted)')\nax2.legend(); ax2.grid(alpha=0.3)\nplt.suptitle('BiLSTM+Attention Training Progress', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_training_curves.png', dpi=150)\nplt.show()\n \n# 2. Confusion matrix\ncm     = confusion_matrix(al, ap)\ncm_pct = cm.astype(float) / cm.sum(axis=1, keepdims=True) * 100\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\nsns.heatmap(cm,     annot=True, fmt='d',   cmap='Oranges', ax=ax1,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax1.set(title='Confusion Matrix (Counts)', ylabel='True', xlabel='Predicted')\nsns.heatmap(cm_pct, annot=True, fmt='.1f', cmap='Oranges', ax=ax2,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax2.set(title='Confusion Matrix (%)', ylabel='True', xlabel='Predicted')\nplt.suptitle(f'BiLSTM+Attention | F1={f1:.4f}  Acc={acc:.4f}',\n             fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_confusion_matrix.png', dpi=150)\nplt.show()\n \n# 3. Per-class bar chart\nmet_df = pd.DataFrame({\n    c: [rep[c]['precision'], rep[c]['recall'], rep[c]['f1-score']]\n    for c in CLASS_NAMES}, index=['Precision', 'Recall', 'F1'])\nfig, ax = plt.subplots(figsize=(12, 5))\nmet_df.T.plot(kind='bar', ax=ax,\n              color=['#3498db', '#2ecc71', '#e67e22'], edgecolor='white')\nax.set(title='BiLSTM+Attention Per-Class Performance',\n       xlabel='Attack Class', ylabel='Score')\nax.set_ylim(0, 1.15); ax.legend(loc='lower right')\nax.set_xticklabels(CLASS_NAMES, rotation=0)\nfor c in ax.containers: ax.bar_label(c, fmt='%.3f', fontsize=8, padding=2)\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_per_class.png', dpi=150)\nplt.show()\n \n# 4. Attention weight visualisation\nmodel.eval()\nsample_x = torch.tensor(X_test[:1], dtype=torch.float32).to(DEVICE)\nwith torch.no_grad():\n    attn_w = model.get_attention_weights(sample_x).cpu().numpy()[0]\n \nfig, ax = plt.subplots(figsize=(8, 3))\nbars = ax.bar(range(SEQ_LEN), attn_w,\n              color=['#e74c3c','#3498db','#2ecc71','#e67e22'][:SEQ_LEN])\nax.set_xticks(range(SEQ_LEN))\nax.set_xticklabels([f'Step {i+1}\\n(feat {i*INPUT_DIM//SEQ_LEN+1}-{(i+1)*INPUT_DIM//SEQ_LEN})'\n                    for i in range(SEQ_LEN)])\nax.set_xlabel('Time Step (feature group)')\nax.set_ylabel('Attention Weight')\nax.set_title('BiLSTM Attention Weights\\n(which feature groups matter most)')\nax.grid(axis='y', alpha=0.3)\nfor bar, val in zip(bars, attn_w):\n    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.002,\n            f'{val:.3f}', ha='center', fontsize=11, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_attention_weights.png', dpi=150)\nplt.show()\n \nprint(\"📊 All plots saved\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:22.733348Z","iopub.execute_input":"2026-05-16T06:53:22.733594Z","iopub.status.idle":"2026-05-16T06:53:25.031866Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x500 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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TZs2tY+kio6OVr9+/bRjxw6FhYXJxcVFzZs3TzQ666233lL//v21e/duFS9eXG3atLmv2xGBjOpu388CAgIUFxenlStXyhjjhArhFE4OxfAISW5UhjHGtG7d2pQsWdIYk/xIKWOM8ff3N6+88ooxxpjBgwebEiVKmISEBPv66dOnG19fXxMfH2+Muflfo7Nly2a8vLxM9erVzaBBg8yePXvS7qSAB1hKrslvv/3WuLq6moiICPu6ffv2GUlm+/bt9tclS5Y0Li4upmzZsqZ79+5mzZo1VpwCkKHcfk1VrVrVdO7c2RjjOFLqxRdfNPXr13fY7s033zSlSpWyvy5YsKBp1qyZQ5+5c+caSQ6jLLp37258fHzM5cuX7W2hoaGme/fuydb4yy+/GEn2bRgphQfN7ddZQkKCWb9+vfH09DTNmjUzksyWLVvsfc+cOWO8vb3NsmXLjDH/u452796d7D5viYuLMx07djSSTEBAgGnWrJmZOnWquXjxor1PmzZtTI0aNVJc++nTp40ks3fvXmPM/0Z8fPzxx/Y+t37HHjhwIMX7BR4kd/t+NnjwYOPm5mayZ89uGjRoYMaPH+8wQv/WdePt7W0yZcrksLi4uDBS6gHESClkCMaYFE0Ce3u/AwcOqFq1ag7b1ahRQ1euXLH/1+kWLVron3/+0ZdffqkGDRpo06ZNqlChQqL/Ig3A0a1r7cCBA8qfP7/y589vX1eqVCn5+fnpwIED9te///67fvrpJ3Xu3FmnTp1S06ZN1bVrV2eVDzjdO++8o08++cR+ndxy4MAB1ahRw6GtRo0aOnLkiMM8NRUrVky0Tx8fHxUpUsT+2t/fX4GBgfL19XVouzUZrHRzZFbTpk1VoEABZc6cWbVq1ZIkRURE3N8JAk709ddfy9fXV15eXmrYsKFat26tjh07ys3NTVWqVLH3y5Ejh0qUKOFwHXp4eKhcuXJ3PYarq6vmzp2rv/76S+PHj1e+fPk0ZswYlS5dWidPnpT0v5FSyTly5IjatGmjwoULK0uWLAoMDJSU+Pq7vZ48efJIksN1DDxM7vb97O2331ZkZKRmzpyp0qVLa+bMmQoKCtLevXsd9rN06VLt3r3bYUnqdycyPkIpZAgHDhxQoUKF7tjn1uR3d+v3b15eXqpfv76GDh2qrVu3qmPHjho+fPj9lAs89FJyTd7OxcVFlSpVUt++ffX5559r3rx5mj17tsLDw9OxSiDjevLJJxUaGqpBgwalavtMmTIlanN3d3d4fevJmf9uu3VrUHR0tEJDQ5UlSxYtWrRIv/zyi1auXCmJydPxYKtTp452796tI0eO6Nq1a/rkk09S/IRLb2/ve3oaZr58+dSuXTtNmzZN+/bt0/Xr1zVz5kz7vu6kadOmOnfunGbNmqWff/5ZP//8s6TE19/t1/Gt2u7lKbjAg+Zu389y5Mih559/XhMmTNCBAweUN29eTZgwwWEf+fPnV9GiRR2Wu12TyJgIpeB0GzZs0N69e9WiRYs79ps8ebJcXFzsj+wtWbKkfb6AW7Zs2aLMmTPrscceS3Y/pUqV4ukNwB3cfk2WLFlSJ06c0IkTJ+zr9+/frwsXLqhUqVLJ7uPWOq41PMrGjRunr776Stu2bbO3lSxZ0v7Y61u2bNmi4sWLy9XVNU2Pf/DgQZ09e1bjxo1TzZo1FRQUxOgLPBQyZcqkokWLqkCBAnJzc5N089qKi4uzBz/Szf+geejQoTv+vpKU4ifqZcuWTXny5LH/bitXrpzCwsKS7Hvr2EOGDFHdunVVsmTJNH0qJ/AwudP3Mw8PDxUpUoS/KR9ibs4uAI+WGzduKDIyUvHx8YqKitLatWs1duxYNWnSRO3bt7f3u3z5siIjIxUbG6vw8HAtXLhQH3/8scaOHauiRYtKkl599VVNmjRJvXr1Us+ePXXo0CENHz5c/fr1k4uLi86ePavnn39enTt3Vrly5ZQ5c2bt2LFD48eP17PPPuustwDIUO52Tbq4uKhs2bJq27atJk2apLi4OL366quqVauWfYh0y5YtVaNGDfvEsOHh4Ro0aJCKFy+uoKAgJ58h4Dy3rp0pU6bY29544w1VqlRJo0ePVuvWrbVt2zZNmzZNH3zwQZofv0CBAvLw8NDUqVPVo0cP/f777xo9enSaHwfICIoVK6Znn31W3bp104cffqjMmTNr4MCBypcv313/7gsMDNS6det06NAh5ciRQ1mzZtWcOXO0e/duNW/eXEWKFNH169c1f/587du3T1OnTpUkDRo0SGXLltWrr76qHj16yMPDQxs3btTzzz+v7NmzK0eOHProo4+UJ08eRUREaODAgVa8FUCGdbfvZ19//bWWLFmiF154QcWLF5cxRl999ZXWrFmjuXPnOrt8pBNCKVhq7dq1ypMnj9zc3JQtWzYFBwdrypQp6tChg1xc/jdwb9iwYRo2bJg8PDwUEBCgqlWrKiwsTHXq1LH3yZcvn9asWaM333xTwcHByp49u7p06aIhQ4ZIuvl0hypVquj999/XsWPHFBsbq/z586tbt24aPHiw5ecOZEQpuSa/+OIL9erVS08++aRcXFzUoEED+x/kkhQaGqpPP/1UY8eO1cWLFxUQEKCnnnpKI0aMsP8XbOBRNWrUKC1dutT+ukKFClq2bJmGDRum0aNHK0+ePBo1alS6PBEvV65cmjdvngYPHqwpU6aoQoUKmjBhgp555pk0PxaQEcydO1d9+vRRkyZNFBMToyeffFJr1qxJdJvrv3Xr1k2bNm1SxYoVdeXKFW3cuFGVK1fWjz/+qB49euiff/6Rr6+vSpcurVWrVtnnZitevLi+/fZbDR48WJUrV5a3t7eqVKmiNm3ayMXFRUuWLFHv3r1VpkwZlShRQlOmTFHt2rUteCeAjOlu389OnjwpHx8fvfHGGzpx4oQ8PT1VrFgxffzxx2rXrp2zy0c6sRnDsxYBAAAAAABgLeaUAgAAAAAAgOUIpQAAAAAAAGA5QikAAAAAAABYjlAKAAAAAAAAliOUAgAAAAAAgOUIpQAAAAAAAGA5QikAAAAAAABYjlAKAAAAAAAAliOUAgAAyEA2bdokm82mCxcupPm+bTabVq1aleb7BQAASA1CKQAAAEnbtm2Tq6urGjdunGjdiBEjVL58+UTtGSnkiYyMVK9evVS4cGF5enoqf/78atq0qcLCwpxdGgAAQJIIpQAAACTNnj1bvXr10ubNm/XPP/84u5x7cvz4cYWEhGjDhg169913tXfvXq1du1Z16tTRa6+95uzyAAAAkkQoBQAAHnlXrlzR0qVL9corr6hx48aaN2+efd28efM0cuRI7dmzRzabTTabTfPmzVNgYKAkqXnz5rLZbPbXx44d07PPPit/f3/5+vqqUqVK+u677xyOd+PGDQ0YMED58+eXp6enihYtqtmzZydZ29WrV9WwYUPVqFEj2Vv6Xn31VdlsNm3fvl0tWrRQ8eLFVbp0afXr108//fRTsuc9YMAAFS9eXD4+PipcuLCGDh2q2NhY+/o9e/aoTp06ypw5s7JkyaKQkBDt2LFDkvTnn3+qadOmypYtmzJlyqTSpUtrzZo1d3mnAQAA/sfN2QUAAAA427JlyxQUFKQSJUropZdeUt++fTVo0CDZbDa1bt1av//+u9auXWsPl7JmzarGjRsrd+7cmjt3rho0aCBXV1dJNwOuRo0a6e2335anp6fmz5+vpk2b6tChQypQoIAkqX379tq2bZumTJmi4OBghYeH68yZM4nqunDhgho3bixfX1+tX79ePj4+ifqcO3dOa9eu1dtvv61MmTIlWu/n55fseWfOnFnz5s1T3rx5tXfvXnXr1k2ZM2fWf/7zH0lS27Zt9fjjj2vGjBlydXXV7t275e7uLkl67bXXFBMTo82bNytTpkzav3+/fH197+2NBwAAjzRCKQAA8MibPXu2XnrpJUlSgwYNdPHiRX3//feqXbu2vL295evrKzc3NwUEBNi38fb2lnQz9Lm9PTg4WMHBwfbXo0eP1sqVK/Xll1+qZ8+eOnz4sJYtW6b169erXr16kqTChQsnqikyMlKtW7dWsWLFtHjxYnl4eCRZ+9GjR2WMUVBQ0D2f95AhQ+z/DgwMVP/+/bVkyRJ7KBUREaE333zTvu9ixYrZ+0dERKhFixYqW7ZssucAAABwJ9y+BwAAHmmHDh3S9u3b1aZNG0mSm5ubWrduneztdHdz5coV9e/fXyVLlpSfn598fX114MABRURESJJ2794tV1dX1apV6477qV+/vooWLaqlS5cmG0hJkjEmVXVK0tKlS1WjRg0FBATI19dXQ4YMsdcpSf369VPXrl1Vr149jRs3TseOHbOv6927t/773/+qRo0aGj58uH777bdU1wEAAB5NhFIAAOCRNnv2bMXFxSlv3rxyc3OTm5ubZsyYoc8++0wXL1685/31799fK1eu1JgxY/TDDz9o9+7dKlu2rGJiYiT9b4TV3TRu3FibN2/W/v3779ivWLFistlsOnjw4D3VuW3bNrVt21aNGjXS119/rV9//VVvvfWWvU7p5lMH9+3bp8aNG2vDhg0qVaqUVq5cKUnq2rWr/vjjD7Vr10579+5VxYoVNXXq1HuqAQAAPNoIpQAAwCMrLi5O8+fP13vvvafdu3fblz179ihv3rz69NNPJUkeHh6Kj49PtL27u3ui9i1btqhjx45q3ry5ypYtq4CAAB0/fty+vmzZskpISND3339/x9rGjRunDh06qG7duncMprJnz67Q0FBNnz5d0dHRidYnNzn61q1bVbBgQb311luqWLGiihUrpj///DNRv+LFi+v111/Xt99+q+eee05z5861r8ufP7969Oihzz//XG+88YZmzZp1x3MCAAC4HaEUAAB4ZH399dc6f/68unTpojJlyjgsLVq0sN/CFxgYqPDwcO3evVtnzpzRjRs37O1hYWGKjIzU+fPnJd0cufT555/bw60XX3xRCQkJ9mMGBgaqQ4cO6ty5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\n"},"metadata":{}},{"name":"stdout","text":"📊 All plots saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── ROC Curves ────────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  ROC-AUC CURVES\")\nprint(f\"{'='*60}\")\n \ny_bin    = label_binarize(al, classes=[0, 1, 2, 3, 4])\ncolors   = ['#e74c3c', '#e67e22', '#2ecc71', '#3498db', '#9b59b6']\nall_aucs = {}\n \n# Plot 1: All classes combined\nfig, ax = plt.subplots(figsize=(10, 8))\nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc     = auc(fpr, tpr)\n    all_aucs[cls_name] = round(roc_auc, 4)\n    ax.plot(fpr, tpr, color=color, lw=2,\n            label=f'{cls_name}  (AUC = {roc_auc:.4f})')\n \nax.plot([0,1],[0,1],'k--',lw=1,label='Random Classifier')\nax.fill_between([0,1],[0,1],alpha=0.05,color='gray')\nax.set_xlim([0.0,1.0]); ax.set_ylim([0.0,1.05])\nax.set_xlabel('False Positive Rate', fontsize=13)\nax.set_ylabel('True Positive Rate', fontsize=13)\nax.set_title('BiLSTM+Attention - ROC Curves (One-vs-Rest)',\n             fontsize=14, fontweight='bold')\nax.legend(loc='lower right', fontsize=11); ax.grid(alpha=0.3)\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_roc_curves.png', dpi=150)\nplt.show()\n \n# Plot 2: Individual subplots + macro average\nfig, axes = plt.subplots(2, 3, figsize=(16, 10))\naxes = axes.flatten()\n \nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc     = auc(fpr, tpr)\n    ax = axes[i]\n    ax.plot(fpr, tpr, color=color, lw=2.5, label=f'AUC = {roc_auc:.4f}')\n    ax.fill_between(fpr, tpr, alpha=0.15, color=color)\n    ax.plot([0,1],[0,1],'k--',lw=1)\n    ax.set_xlim([0.0,1.0]); ax.set_ylim([0.0,1.05])\n    ax.set_xlabel('False Positive Rate')\n    ax.set_ylabel('True Positive Rate')\n    ax.set_title(f'{cls_name}', fontsize=13, fontweight='bold')\n    ax.legend(loc='lower right'); ax.grid(alpha=0.3)\n \n# Macro average\nfpr_all = np.linspace(0, 1, 200)\ntpr_all = np.zeros_like(fpr_all)\nfor i in range(NUM_CLASSES):\n    fpr_i, tpr_i, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    tpr_all += np.interp(fpr_all, fpr_i, tpr_i)\ntpr_all   /= NUM_CLASSES\nmacro_auc  = auc(fpr_all, tpr_all)\nall_aucs['Macro-Average'] = round(macro_auc, 4)\n \naxes[5].plot(fpr_all, tpr_all, color='black', lw=2.5,\n             label=f'Macro AUC = {macro_auc:.4f}')\naxes[5].fill_between(fpr_all, tpr_all, alpha=0.1, color='black')\naxes[5].plot([0,1],[0,1],'k--',lw=1)\naxes[5].set_xlim([0.0,1.0]); axes[5].set_ylim([0.0,1.05])\naxes[5].set_xlabel('False Positive Rate')\naxes[5].set_ylabel('True Positive Rate')\naxes[5].set_title('Macro-Average', fontsize=13, fontweight='bold')\naxes[5].legend(loc='lower right'); axes[5].grid(alpha=0.3)\n \nplt.suptitle('BiLSTM+Attention - ROC Curves Per Class',\n             fontsize=15, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/bilstm_roc_per_class.png', dpi=150)\nplt.show()\n \nprint(f\"\\n  ROC-AUC Summary:\")\nprint(f\"  {'─'*38}\")\nfor cls_name, auc_val in all_aucs.items():\n    quality = \"✅ Excellent\" if auc_val >= 0.99 else \\\n              \"✅ Very Good\" if auc_val >= 0.95 else \\\n              \"⚠ Good\"      if auc_val >= 0.90 else \"❌ Needs work\"\n    print(f\"    {cls_name:15s}: AUC = {auc_val:.4f}  {quality}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:25.033077Z","iopub.execute_input":"2026-05-16T06:53:25.033420Z","iopub.status.idle":"2026-05-16T06:53:27.354702Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  ROC-AUC CURVES\n============================================================\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}},{"name":"stdout","text":"\n  ROC-AUC Summary:\n  ──────────────────────────────────────\n    DDoS           : AUC = 1.0000  ✅ Excellent\n    DoS            : AUC = 1.0000  ✅ Excellent\n    Normal         : AUC = 0.9995  ✅ Excellent\n    PortScan       : AUC = 0.9996  ✅ Excellent\n    SSH            : AUC = 1.0000  ✅ Excellent\n    Macro-Average  : AUC = 0.9997  ✅ Excellent\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"results = {\n    'model': 'BiLSTM+Attention',\n    'architecture': {\n        'input_dim':  INPUT_DIM,\n        'hidden_dim': HIDDEN_DIM,\n        'num_layers': NUM_LAYERS,\n        'seq_len':    SEQ_LEN,\n        'parameters': n_params,\n    },\n    'test_results': {\n        'accuracy':  round(acc,  4),\n        'precision': round(prec, 4),\n        'recall':    round(rec,  4),\n        'f1_score':  round(f1,   4),\n    },\n    'best_val_f1':    round(best_f1, 4),\n    'epochs':         EPOCHS,\n    'cross_platform': cross_results,\n    'per_class': {\n        c: {'precision': round(rep[c]['precision'], 4),\n            'recall':    round(rep[c]['recall'],    4),\n            'f1':        round(rep[c]['f1-score'],  4)}\n        for c in CLASS_NAMES},\n    'roc_auc': all_aucs,\n}\n \nwith open('/kaggle/working/bilstm_results.json', 'w') as f:\n    json.dump(results, f, indent=2)\ntorch.save(model.state_dict(), '/kaggle/working/bilstm_weights.pth')\n \nprint(f\"\\n  Saved:\")\nprint(f\"    📄 bilstm_results.json\")\nprint(f\"    💾 bilstm_weights.pth\")\nprint(f\"    📊 bilstm_training_curves.png\")\nprint(f\"    📊 bilstm_confusion_matrix.png\")\nprint(f\"    📊 bilstm_per_class.png\")\nprint(f\"    📊 bilstm_attention_weights.png  ← unique to BiLSTM!\")\nprint(f\"    📊 bilstm_roc_curves.png\")\nprint(f\"    📊 bilstm_roc_per_class.png\")\n ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.355795Z","iopub.execute_input":"2026-05-16T06:53:27.356005Z","iopub.status.idle":"2026-05-16T06:53:27.371690Z"}},"outputs":[{"name":"stdout","text":"\n  Saved:\n    📄 bilstm_results.json\n    💾 bilstm_weights.pth\n    📊 bilstm_training_curves.png\n    📊 bilstm_confusion_matrix.png\n    📊 bilstm_per_class.png\n    📊 bilstm_attention_weights.png  ← unique to BiLSTM!\n    📊 bilstm_roc_curves.png\n    📊 bilstm_roc_per_class.png\n","output_type":"stream"}],"execution_count":null},{"cell_type":"markdown","source":"# **GNN**","metadata":{}},{"cell_type":"code","source":"# ──  Hyperparameters ────────────────────────────────────────\nHIDDEN_DIM  = 128     # GNN hidden dimension\nNUM_LAYERS  = 3       # GraphSAGE layers\nDROPOUT     = 0.3\nBATCH_SIZE  = 512     # records per mini-batch graph\nK_NEIGHBORS = 8       # k-NN graph connectivity\nEPOCHS      = 50\nLR          = 1e-3\n \nprint(f\"\\n   hidden_dim={HIDDEN_DIM} | num_layers={NUM_LAYERS} | k={K_NEIGHBORS}\")\nprint(f\"   batch={BATCH_SIZE} | epochs={EPOCHS} | lr={LR}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.372813Z","iopub.execute_input":"2026-05-16T06:53:27.373745Z","iopub.status.idle":"2026-05-16T06:53:27.378218Z"}},"outputs":[{"name":"stdout","text":"\n   hidden_dim=128 | num_layers=3 | k=8\n   batch=512 | epochs=50 | lr=0.001\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Data Preparation ───────────────────────────────────────\nscaler   = StandardScaler()\nX_scaled = scaler.fit_transform(X).astype(np.float32)\n \nX_temp, X_test, y_temp, y_test = train_test_split(\n    X_scaled, y, test_size=0.15, random_state=42, stratify=y)\nX_train, X_val, y_train, y_val = train_test_split(\n    X_temp, y_temp, test_size=0.176, random_state=42, stratify=y_temp)\n \nprint(f\"\\n   Train:{len(X_train):,} | Val:{len(X_val):,} | Test:{len(X_test):,}\")\n \ndef make_loader(X, y, shuffle=True):\n    return DataLoader(\n        TensorDataset(torch.tensor(X, dtype=torch.float32),\n                      torch.tensor(y, dtype=torch.long)),\n        batch_size=BATCH_SIZE, shuffle=shuffle,\n        pin_memory=(DEVICE.type == 'cuda'))\n \ntrain_loader = make_loader(X_train, y_train)\nval_loader   = make_loader(X_val,   y_val,   shuffle=False)\ntest_loader  = make_loader(X_test,  y_test,  shuffle=False)\n \nhas_platform = 'cloud_platform' in df.columns\nif has_platform:\n    az   = df['cloud_platform'] == 'Azure'\n    aw   = df['cloud_platform'] == 'AWS'\n    X_az = scaler.transform(df[feature_cols][az].fillna(0).values.astype(np.float32))\n    X_aw = scaler.transform(df[feature_cols][aw].fillna(0).values.astype(np.float32))\n    y_az = df[LABEL][az].values.astype(np.int64)\n    y_aw = df[LABEL][aw].values.astype(np.int64)\n    print(f\"   Azure:{len(X_az):,} | AWS:{len(X_aw):,}\")\nprint(\"✅ Data ready\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.379027Z","iopub.execute_input":"2026-05-16T06:53:27.379666Z","iopub.status.idle":"2026-05-16T06:53:27.740508Z"}},"outputs":[{"name":"stdout","text":"\n   Train:174,278 | Val:37,225 | Test:37,325\n   Azure:146,595 | AWS:102,233\n✅ Data ready\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Graph Construction ─────────────────────────────────────\ndef build_knn_adjacency(x, k=8):\n    \"\"\"\n    Build a k-Nearest Neighbour adjacency matrix for a batch of nodes.\n \n    Each log record is a node. Two records are connected if they are\n    among each other's k nearest neighbours in feature space.\n \n    This captures:\n      - Records with similar traffic patterns\n      - Temporal co-occurrence of attacks\n      - Cross-platform behavioural similarity\n \n    Returns: Normalised adjacency matrix (D^-1/2 A D^-1/2)\n    \"\"\"\n    with torch.no_grad():\n        # Cosine similarity between all pairs\n        norm  = x / (x.norm(dim=1, keepdim=True) + 1e-8)\n        sim   = torch.mm(norm, norm.t())               # (N, N)\n \n        # Keep top-k neighbours per node\n        k     = min(k, sim.size(0) - 1)\n        _, idx = sim.topk(k + 1, dim=1)               # +1 to include self\n        adj   = torch.zeros_like(sim)\n        adj.scatter_(1, idx, 1.0)\n \n        # Symmetrise (undirected graph)\n        adj   = torch.clamp(adj + adj.t(), max=1.0)\n \n        # Symmetric normalisation: D^-1/2 A D^-1/2\n        deg   = adj.sum(dim=1).clamp(min=1)\n        d_inv = deg.pow(-0.5)\n        adj   = d_inv.unsqueeze(1) * adj * d_inv.unsqueeze(0)\n \n    return adj\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.741832Z","iopub.execute_input":"2026-05-16T06:53:27.742450Z","iopub.status.idle":"2026-05-16T06:53:27.748774Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ──ForensicGNN Model ──────────────────────────────────────\nclass GraphSAGELayer(nn.Module):\n    \"\"\"\n    GraphSAGE-style convolution layer with mean aggregation.\n \n    For each node:\n      h_v = σ( W · CONCAT(h_v, MEAN(h_u for u in N(v))) )\n \n    Uses residual connection for stable deep graph training.\n    \"\"\"\n    def __init__(self, in_dim, out_dim, dropout=0.3):\n        super().__init__()\n        # Self transformation\n        self.self_linear  = nn.Linear(in_dim,  out_dim)\n        # Neighbour aggregation\n        self.neigh_linear = nn.Linear(in_dim,  out_dim)\n        # Combine\n        self.combine      = nn.Linear(out_dim * 2, out_dim)\n        self.norm         = nn.LayerNorm(out_dim)\n        self.dropout      = nn.Dropout(dropout)\n        # Residual projection if dims differ\n        self.residual     = nn.Linear(in_dim, out_dim) if in_dim != out_dim else nn.Identity()\n \n        nn.init.xavier_uniform_(self.self_linear.weight)\n        nn.init.xavier_uniform_(self.neigh_linear.weight)\n        nn.init.xavier_uniform_(self.combine.weight)\n \n    def forward(self, x, adj):\n        # x   : (N, in_dim)\n        # adj : (N, N) normalised adjacency\n \n        # Neighbour aggregation via adjacency (mean)\n        neigh_agg = torch.mm(adj, x)                          # (N, in_dim)\n \n        # Transform self and neighbours separately\n        self_h    = self.self_linear(x)                       # (N, out_dim)\n        neigh_h   = self.neigh_linear(neigh_agg)              # (N, out_dim)\n \n        # Combine\n        h         = self.combine(torch.cat([self_h, neigh_h], dim=1))  # (N, out_dim)\n        h         = self.norm(F.relu(h))\n        h         = self.dropout(h)\n \n        # Residual connection\n        return h + self.residual(x)\n \n \nclass ForensicGNN(nn.Module):\n    \"\"\"\n    Graph Neural Network for Multi-Cloud Forensic Log Classification.\n \n    HIGH NOVELTY:\n      - First GNN approach for multi-cloud forensic analysis\n      - Models log records as graph nodes with k-NN edges\n      - Message passing captures neighbourhood attack patterns\n      - Detects coordinated attacks (DDoS) via graph connectivity\n      - Cross-platform: same graph structure on Azure + AWS logs\n \n    Input  : (B, INPUT_DIM=20)  ← AE bottleneck features\n    Output : (B, NUM_CLASSES=5) ← attack type logits\n    \"\"\"\n    def __init__(self, input_dim, num_classes, hidden_dim=128,\n                 num_layers=3, dropout=0.3, k=8):\n        super().__init__()\n        self.k = k\n \n        # Input projection\n        self.input_proj = nn.Sequential(\n            nn.Linear(input_dim, hidden_dim),\n            nn.LayerNorm(hidden_dim),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n        )\n \n        # GraphSAGE layers\n        self.gnn_layers = nn.ModuleList([\n            GraphSAGELayer(hidden_dim, hidden_dim, dropout)\n            for _ in range(num_layers)\n        ])\n \n        # Classifier head\n        self.classifier = nn.Sequential(\n            nn.Linear(hidden_dim * 2, hidden_dim),   # *2: node + mean-pool\n            nn.LayerNorm(hidden_dim),\n            nn.ReLU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_dim, hidden_dim // 2),\n            nn.ReLU(),\n            nn.Dropout(dropout / 2),\n            nn.Linear(hidden_dim // 2, num_classes),\n        )\n \n        for m in self.classifier.modules():\n            if isinstance(m, nn.Linear):\n                nn.init.xavier_uniform_(m.weight)\n                if m.bias is not None: nn.init.zeros_(m.bias)\n \n    def forward(self, x):\n        # Build k-NN graph for this batch\n        adj = build_knn_adjacency(x, k=self.k)     # (B, B)\n \n        # Project input\n        h   = self.input_proj(x)                   # (B, hidden_dim)\n \n        # Graph message passing\n        for layer in self.gnn_layers:\n            h = layer(h, adj)                       # (B, hidden_dim)\n \n        # Readout: concat node features with global mean pool\n        global_h = h.mean(dim=0, keepdim=True).expand_as(h)  # (B, hidden_dim)\n        h_out    = torch.cat([h, global_h], dim=1)            # (B, hidden_dim*2)\n \n        return self.classifier(h_out)               # (B, num_classes)\n \n \nmodel    = ForensicGNN(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM,\n                       NUM_LAYERS, DROPOUT, K_NEIGHBORS).to(DEVICE)\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"\\n  ForensicGNN ready | Parameters: {n_params:,}\")\nprint(f\"  Flow: {INPUT_DIM}d → Input Proj → {NUM_LAYERS}×GraphSAGE({HIDDEN_DIM}) → Classifier → {NUM_CLASSES}\")\nprint(f\"  Graph: k={K_NEIGHBORS} neighbours per node | Symmetric normalised adjacency\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.749663Z","iopub.execute_input":"2026-05-16T06:53:27.749899Z","iopub.status.idle":"2026-05-16T06:53:27.777765Z"}},"outputs":[{"name":"stdout","text":"\n  ForensicGNN ready | Parameters: 243,205\n  Flow: 20d → Input Proj → 3×GraphSAGE(128) → Classifier → 5\n  Graph: k=8 neighbours per node | Symmetric normalised adjacency\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ── Training Setup ─────────────────────────────────────────\n\nmodel    = ForensicGNN(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM, \n                       NUM_LAYERS, DROPOUT, K_NEIGHBORS).to(DEVICE)\nn_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\nprint(f\"  ForensicGNN ready | Parameters: {n_params:,}\")\n\ncounts  = np.bincount(y_train, minlength=NUM_CLASSES)\nweights = torch.tensor(1.0/(counts+1e-6), dtype=torch.float32).to(DEVICE)\nweights = weights / weights.sum() * NUM_CLASSES\n\ncriterion = nn.CrossEntropyLoss(weight=weights, label_smoothing=0.05)\noptimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n    optimizer, T_max=EPOCHS, eta_min=1e-5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.778820Z","iopub.execute_input":"2026-05-16T06:53:27.779050Z","iopub.status.idle":"2026-05-16T06:53:27.795646Z"}},"outputs":[{"name":"stdout","text":"  ForensicGNN ready | Parameters: 243,205\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Training Loop ──────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TRAINING ForensicGNN ({EPOCHS} epochs)\")\nprint(f\"{'='*60}\")\nprint(f\"  Note: Each batch builds a {BATCH_SIZE}-node graph on the fly\")\n \nhistory    = {'train_loss':[], 'val_loss':[], 'train_f1':[], 'val_f1':[]}\nbest_f1    = 0.0\nbest_state = None\nt0         = time.time()\n \nfor epoch in range(EPOCHS):\n    # Train\n    model.train()\n    tl, tp, tlab = 0, [], []\n    for bx, by in train_loader:\n        bx, by = bx.to(DEVICE), by.to(DEVICE)\n        out    = model(bx)\n        loss   = criterion(out, by)\n        optimizer.zero_grad(); loss.backward()\n        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n        optimizer.step()\n        tl += loss.item()\n        tp.extend(out.argmax(1).cpu().numpy())\n        tlab.extend(by.cpu().numpy())\n \n    scheduler.step()\n \n    # Validate\n    model.eval()\n    vl, vp, vlab = 0, [], []\n    with torch.no_grad():\n        for bx, by in val_loader:\n            bx, by = bx.to(DEVICE), by.to(DEVICE)\n            out    = model(bx)\n            vl    += criterion(out, by).item()\n            vp.extend(out.argmax(1).cpu().numpy())\n            vlab.extend(by.cpu().numpy())\n \n    tl_avg = tl / len(train_loader)\n    vl_avg = vl / len(val_loader)\n    tf1    = f1_score(tlab, tp,  average='weighted', zero_division=0)\n    vf1    = f1_score(vlab, vp,  average='weighted', zero_division=0)\n    vacc   = accuracy_score(vlab, vp)\n \n    history['train_loss'].append(tl_avg); history['val_loss'].append(vl_avg)\n    history['train_f1'].append(tf1);      history['val_f1'].append(vf1)\n \n    if vf1 > best_f1:\n        best_f1    = vf1\n        best_state = {k: v.clone() for k, v in model.state_dict().items()}\n \n    if (epoch + 1) % 5 == 0 or epoch == 0:\n        print(f\"  Ep {epoch+1:3d}/{EPOCHS} | \"\n              f\"Loss {tl_avg:.4f}/{vl_avg:.4f} | \"\n              f\"F1 {tf1:.4f}/{vf1:.4f} | \"\n              f\"Acc {vacc:.4f} | {time.time()-t0:.0f}s\")\n \nmodel.load_state_dict(best_state)\nprint(f\"\\n  ★ Best Val F1 : {best_f1:.4f}\")\nprint(f\"  Total time    : {(time.time()-t0)/60:.1f} min\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:53:27.796732Z","iopub.execute_input":"2026-05-16T06:53:27.797056Z","iopub.status.idle":"2026-05-16T06:56:48.449480Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TRAINING ForensicGNN (50 epochs)\n============================================================\n  Note: Each batch builds a 512-node graph on the fly\n  Ep   1/50 | Loss 0.4723/0.4043 | F1 0.9657/0.9839 | Acc 0.9839 | 4s\n  Ep   5/50 | Loss 0.3900/0.3766 | F1 0.9908/0.9927 | Acc 0.9927 | 20s\n  Ep  10/50 | Loss 0.3807/0.3718 | F1 0.9919/0.9941 | Acc 0.9942 | 40s\n  Ep  15/50 | Loss 0.3750/0.3633 | F1 0.9920/0.9938 | Acc 0.9938 | 60s\n  Ep  20/50 | Loss 0.3679/0.3546 | F1 0.9932/0.9962 | Acc 0.9962 | 80s\n  Ep  25/50 | Loss 0.3594/0.3511 | F1 0.9960/0.9981 | Acc 0.9981 | 101s\n  Ep  30/50 | Loss 0.3577/0.3506 | F1 0.9967/0.9975 | Acc 0.9975 | 121s\n  Ep  35/50 | Loss 0.3546/0.3478 | F1 0.9975/0.9985 | Acc 0.9985 | 140s\n  Ep  40/50 | Loss 0.3521/0.3468 | F1 0.9982/0.9987 | Acc 0.9987 | 160s\n  Ep  45/50 | Loss 0.3510/0.3468 | F1 0.9984/0.9986 | Acc 0.9986 | 180s\n  Ep  50/50 | Loss 0.3511/0.3466 | F1 0.9984/0.9987 | Acc 0.9987 | 201s\n\n  ★ Best Val F1 : 0.9987\n  Total time    : 3.3 min\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Test Evaluation ────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  TEST SET EVALUATION\")\nprint(f\"{'='*60}\")\n \nmodel.eval()\nap, al, all_probs = [], [], []\nwith torch.no_grad():\n    for bx, by in test_loader:\n        out   = model(bx.to(DEVICE))\n        probs = F.softmax(out, dim=1)\n        ap.extend(out.argmax(1).cpu().numpy())\n        al.extend(by.numpy())\n        all_probs.extend(probs.cpu().numpy())\n \nall_probs = np.array(all_probs)\nacc  = accuracy_score(al, ap)\nprec = precision_score(al, ap, average='weighted', zero_division=0)\nrec  = recall_score(al,  ap,   average='weighted', zero_division=0)\nf1   = f1_score(al,      ap,   average='weighted', zero_division=0)\nrep  = classification_report(al, ap, target_names=CLASS_NAMES,\n                              digits=4, output_dict=True)\n \nprint(f\"\\n  Accuracy  : {acc:.4f}\")\nprint(f\"  Precision : {prec:.4f}\")\nprint(f\"  Recall    : {rec:.4f}\")\nprint(f\"  F1-Score  : {f1:.4f}\")\nprint(f\"\\n{classification_report(al, ap, target_names=CLASS_NAMES, digits=4)}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:56:48.450600Z","iopub.execute_input":"2026-05-16T06:56:48.450861Z","iopub.status.idle":"2026-05-16T06:56:49.003195Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  TEST SET EVALUATION\n============================================================\n\n  Accuracy  : 0.9985\n  Precision : 0.9985\n  Recall    : 0.9985\n  F1-Score  : 0.9985\n\n              precision    recall  f1-score   support\n\n        DDoS     0.9992    0.9990    0.9991     15311\n         DoS     0.9981    1.0000    0.9990     12802\n      Normal     0.9967    0.9882    0.9924      2786\n    PortScan     0.9971    0.9976    0.9974      3811\n         SSH     1.0000    1.0000    1.0000      2615\n\n    accuracy                         0.9985     37325\n   macro avg     0.9982    0.9970    0.9976     37325\nweighted avg     0.9985    0.9985    0.9985     37325\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Cross-Platform Validation (H2) ────────────────────────\ncross_results = {}\nif has_platform:\n    print(f\"\\n{'='*60}\")\n    print(f\"  CROSS-PLATFORM VALIDATION (Thesis H2)\")\n    print(f\"{'='*60}\")\n    for tr_name, Xtr, ytr, te_name, Xte, yte in [\n        ('Azure', X_az, y_az, 'AWS',   X_aw, y_aw),\n        ('AWS',   X_aw, y_aw, 'Azure', X_az, y_az)]:\n \n        m2  = ForensicGNN(INPUT_DIM, NUM_CLASSES, HIDDEN_DIM,\n                          NUM_LAYERS, DROPOUT, K_NEIGHBORS).to(DEVICE)\n        o2  = torch.optim.AdamW(m2.parameters(), lr=LR, weight_decay=1e-4)\n        s2  = torch.optim.lr_scheduler.CosineAnnealingLR(o2, T_max=EPOCHS, eta_min=1e-5)\n        cr2 = nn.CrossEntropyLoss(label_smoothing=0.05)\n        dl2 = make_loader(Xtr, ytr)\n        te2 = make_loader(Xte, yte, shuffle=False)\n \n        for ep in range(EPOCHS):\n            m2.train()\n            for bx, by in dl2:\n                bx, by = bx.to(DEVICE), by.to(DEVICE)\n                l = cr2(m2(bx), by)\n                o2.zero_grad(); l.backward()\n                torch.nn.utils.clip_grad_norm_(m2.parameters(), 1.0)\n                o2.step()\n            s2.step()\n \n        m2.eval(); cp, cl = [], []\n        with torch.no_grad():\n            for bx, by in te2:\n                cp.extend(m2(bx.to(DEVICE)).argmax(1).cpu().numpy())\n                cl.extend(by.numpy())\n \n        cf1 = f1_score(cl, cp, average='weighted', zero_division=0)\n        cac = accuracy_score(cl, cp)\n        key = f\"{tr_name}→{te_name}\"\n        cross_results[key] = {'f1': round(cf1,4), 'accuracy': round(cac,4)}\n        print(f\"\\n  {key}: Acc={cac:.4f}  F1={cf1:.4f}\")\n        print(classification_report(cl, cp, target_names=CLASS_NAMES, digits=4))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T06:56:49.004359Z","iopub.execute_input":"2026-05-16T06:56:49.005096Z","iopub.status.idle":"2026-05-16T07:00:45.437631Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  CROSS-PLATFORM VALIDATION (Thesis H2)\n============================================================\n\n  Azure→AWS: Acc=0.4844  F1=0.5364\n              precision    recall  f1-score   support\n\n        DDoS     0.2532    0.7115    0.3735     17413\n         DoS     0.9988    0.5000    0.6664     69664\n      Normal     0.3774    0.5333    0.4420      4238\n    PortScan     0.0031    0.0036    0.0034     10800\n         SSH     0.0000    0.0000    0.0000       118\n\n    accuracy                         0.4844    102233\n   macro avg     0.3265    0.3497    0.2970    102233\nweighted avg     0.7397    0.4844    0.5364    102233\n\n\n  AWS→Azure: Acc=0.1675  F1=0.1284\n              precision    recall  f1-score   support\n\n        DDoS     0.7558    0.0540    0.1007     84658\n         DoS     0.1336    1.0000    0.2357     15683\n      Normal     0.9826    0.3002    0.4598     14336\n    PortScan     0.0000    0.0000    0.0000     14605\n         SSH     0.0000    0.0000    0.0000     17313\n\n    accuracy                         0.1675    146595\n   macro avg     0.3744    0.2708    0.1592    146595\nweighted avg     0.5469    0.1675    0.1284    146595\n\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Plots ─────────────────────────────────────────────────\n# 1. Training curves\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\nax1.plot(history['train_loss'], color='#e74c3c', lw=2, label='Train')\nax1.plot(history['val_loss'],   color='#9b59b6', lw=2, label='Val')\nax1.set(title='ForensicGNN Loss', xlabel='Epoch', ylabel='Loss')\nax1.legend(); ax1.grid(alpha=0.3)\n \nax2.plot(history['train_f1'], color='#e74c3c', lw=2, label='Train')\nax2.plot(history['val_f1'],   color='#9b59b6', lw=2, label='Val')\nax2.set(title='ForensicGNN F1-Score', xlabel='Epoch', ylabel='F1 (weighted)')\nax2.legend(); ax2.grid(alpha=0.3)\nplt.suptitle('ForensicGNN Training Progress', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_training_curves.png', dpi=150)\nplt.show()\n \n# 2. Confusion matrix\ncm     = confusion_matrix(al, ap)\ncm_pct = cm.astype(float) / cm.sum(axis=1, keepdims=True) * 100\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\nsns.heatmap(cm,     annot=True, fmt='d',   cmap='Purples', ax=ax1,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax1.set(title='Confusion Matrix (Counts)', ylabel='True', xlabel='Predicted')\nsns.heatmap(cm_pct, annot=True, fmt='.1f', cmap='Purples', ax=ax2,\n            xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)\nax2.set(title='Confusion Matrix (%)', ylabel='True', xlabel='Predicted')\nplt.suptitle(f'ForensicGNN | F1={f1:.4f}  Acc={acc:.4f}',\n             fontsize=13, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_confusion_matrix.png', dpi=150)\nplt.show()\n \n# 3. Per-class bar chart\nmet_df = pd.DataFrame({\n    c: [rep[c]['precision'], rep[c]['recall'], rep[c]['f1-score']]\n    for c in CLASS_NAMES}, index=['Precision','Recall','F1'])\nfig, ax = plt.subplots(figsize=(12, 5))\nmet_df.T.plot(kind='bar', ax=ax,\n              color=['#3498db','#2ecc71','#9b59b6'], edgecolor='white')\nax.set(title='ForensicGNN Per-Class Performance',\n       xlabel='Attack Class', ylabel='Score')\nax.set_ylim(0, 1.15); ax.legend(loc='lower right')\nax.set_xticklabels(CLASS_NAMES, rotation=0)\nfor c in ax.containers: ax.bar_label(c, fmt='%.3f', fontsize=8, padding=2)\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_per_class.png', dpi=150)\nplt.show()\n \n# 4. Graph structure visualisation (k-NN adjacency sample)\nsample_x   = torch.tensor(X_test[:50], dtype=torch.float32).to(DEVICE)\nwith torch.no_grad():\n    adj_sample = build_knn_adjacency(sample_x, k=K_NEIGHBORS).cpu().numpy()\nsample_y   = y_test[:50]\n \nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n \n# Adjacency heatmap\nsns.heatmap(adj_sample, cmap='viridis', ax=ax1,\n            xticklabels=False, yticklabels=False,\n            cbar_kws={'label': 'Edge Weight'})\nax1.set_title('k-NN Graph Adjacency Matrix\\n(50 sample nodes)', fontsize=12)\nax1.set_xlabel('Node Index'); ax1.set_ylabel('Node Index')\n \n# Node degree distribution\ndegrees = (adj_sample > 0).sum(axis=1)\nax2.hist(degrees, bins=20, color='#9b59b6', edgecolor='white', alpha=0.8)\nax2.axvline(degrees.mean(), color='red', linestyle='--', lw=2,\n            label=f'Mean degree = {degrees.mean():.1f}')\nax2.set_title('Node Degree Distribution\\n(k-NN Graph)', fontsize=12)\nax2.set_xlabel('Node Degree'); ax2.set_ylabel('Count')\nax2.legend()\n \nplt.suptitle('ForensicGNN - Graph Structure Analysis', fontsize=14, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_graph_structure.png', dpi=150)\nplt.show()\n \nprint(\"📊 All plots saved\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T07:00:45.438883Z","iopub.execute_input":"2026-05-16T07:00:45.439244Z","iopub.status.idle":"2026-05-16T07:00:47.997059Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1400x500 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x600 with 3 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All plots saved\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  ROC Curves ────────────────────────────────────────────\nprint(f\"\\n{'='*60}\")\nprint(f\"  ROC-AUC CURVES\")\nprint(f\"{'='*60}\")\n \ny_bin    = label_binarize(al, classes=[0, 1, 2, 3, 4])\ncolors   = ['#e74c3c','#e67e22','#2ecc71','#3498db','#9b59b6']\nall_aucs = {}\n \n# Plot 1: All classes combined\nfig, ax = plt.subplots(figsize=(10, 8))\nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc     = auc(fpr, tpr)\n    all_aucs[cls_name] = round(roc_auc, 4)\n    ax.plot(fpr, tpr, color=color, lw=2,\n            label=f'{cls_name}  (AUC = {roc_auc:.4f})')\n \nax.plot([0,1],[0,1],'k--',lw=1,label='Random Classifier')\nax.fill_between([0,1],[0,1],alpha=0.05,color='gray')\nax.set_xlim([0.0,1.0]); ax.set_ylim([0.0,1.05])\nax.set_xlabel('False Positive Rate', fontsize=13)\nax.set_ylabel('True Positive Rate',  fontsize=13)\nax.set_title('ForensicGNN - ROC Curves (One-vs-Rest)',\n             fontsize=14, fontweight='bold')\nax.legend(loc='lower right', fontsize=11); ax.grid(alpha=0.3)\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_roc_curves.png', dpi=150)\nplt.show()\n \n# Plot 2: Individual subplots + macro\nfig, axes = plt.subplots(2, 3, figsize=(16, 10))\naxes = axes.flatten()\n \nfor i, (cls_name, color) in enumerate(zip(CLASS_NAMES, colors)):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc     = auc(fpr, tpr)\n    ax = axes[i]\n    ax.plot(fpr, tpr, color=color, lw=2.5, label=f'AUC = {roc_auc:.4f}')\n    ax.fill_between(fpr, tpr, alpha=0.15, color=color)\n    ax.plot([0,1],[0,1],'k--',lw=1)\n    ax.set_xlim([0.0,1.0]); ax.set_ylim([0.0,1.05])\n    ax.set_xlabel('False Positive Rate')\n    ax.set_ylabel('True Positive Rate')\n    ax.set_title(f'{cls_name}', fontsize=13, fontweight='bold')\n    ax.legend(loc='lower right'); ax.grid(alpha=0.3)\n \n# Macro average\nfpr_all = np.linspace(0, 1, 200); tpr_all = np.zeros_like(fpr_all)\nfor i in range(NUM_CLASSES):\n    fpr_i, tpr_i, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    tpr_all += np.interp(fpr_all, fpr_i, tpr_i)\ntpr_all   /= NUM_CLASSES\nmacro_auc  = auc(fpr_all, tpr_all)\nall_aucs['Macro-Average'] = round(macro_auc, 4)\n \naxes[5].plot(fpr_all, tpr_all, color='#9b59b6', lw=2.5,\n             label=f'Macro AUC = {macro_auc:.4f}')\naxes[5].fill_between(fpr_all, tpr_all, alpha=0.15, color='#9b59b6')\naxes[5].plot([0,1],[0,1],'k--',lw=1)\naxes[5].set_xlim([0.0,1.0]); axes[5].set_ylim([0.0,1.05])\naxes[5].set_xlabel('False Positive Rate')\naxes[5].set_ylabel('True Positive Rate')\naxes[5].set_title('Macro-Average', fontsize=13, fontweight='bold')\naxes[5].legend(loc='lower right'); axes[5].grid(alpha=0.3)\n \nplt.suptitle('ForensicGNN - ROC Curves Per Class',\n             fontsize=15, fontweight='bold')\nplt.tight_layout()\nplt.savefig('/kaggle/working/gnn_roc_per_class.png', dpi=150)\nplt.show()\n \nprint(f\"\\n  ROC-AUC Summary:\")\nprint(f\"  {'─'*38}\")\nfor cls_name, auc_val in all_aucs.items():\n    quality = \"✅ Excellent\" if auc_val >= 0.99 else \\\n              \"✅ Very Good\" if auc_val >= 0.95 else \\\n              \"⚠ Good\"      if auc_val >= 0.90 else \"❌ Needs work\"\n    print(f\"    {cls_name:15s}: AUC = {auc_val:.4f}  {quality}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T07:00:47.998363Z","iopub.execute_input":"2026-05-16T07:00:47.999032Z","iopub.status.idle":"2026-05-16T07:00:50.543927Z"}},"outputs":[{"name":"stdout","text":"\n============================================================\n  ROC-AUC CURVES\n============================================================\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}},{"name":"stdout","text":"\n  ROC-AUC Summary:\n  ──────────────────────────────────────\n    DDoS           : AUC = 0.9997  ✅ Excellent\n    DoS            : AUC = 0.9998  ✅ Excellent\n    Normal         : AUC = 0.9992  ✅ Excellent\n    PortScan       : AUC = 0.9999  ✅ Excellent\n    SSH            : AUC = 1.0000  ✅ Excellent\n    Macro-Average  : AUC = 0.9988  ✅ Excellent\n","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"# ──  Save Results ──────────────────────────────────────────\nresults = {\n    'model': 'ForensicGNN',\n    'architecture': {\n        'input_dim':  INPUT_DIM,\n        'hidden_dim': HIDDEN_DIM,\n        'num_layers': NUM_LAYERS,\n        'k_neighbors':K_NEIGHBORS,\n        'parameters': n_params,\n    },\n    'test_results': {\n        'accuracy':  round(acc,  4),\n        'precision': round(prec, 4),\n        'recall':    round(rec,  4),\n        'f1_score':  round(f1,   4),\n    },\n    'best_val_f1':    round(best_f1, 4),\n    'epochs':         EPOCHS,\n    'cross_platform': cross_results,\n    'per_class': {\n        c: {'precision': round(rep[c]['precision'], 4),\n            'recall':    round(rep[c]['recall'],    4),\n            'f1':        round(rep[c]['f1-score'],  4)}\n        for c in CLASS_NAMES},\n    'roc_auc': all_aucs,\n}\n \nwith open('/kaggle/working/gnn_results.json', 'w') as f:\n    json.dump(results, f, indent=2)\ntorch.save(model.state_dict(), '/kaggle/working/gnn_weights.pth')\n \nprint(f\"\\n  Saved:\")\nprint(f\"    📄 gnn_results.json\")\nprint(f\"    💾 gnn_weights.pth\")\nprint(f\"    📊 gnn_training_curves.png\")\nprint(f\"    📊 gnn_confusion_matrix.png\")\nprint(f\"    📊 gnn_per_class.png\")\nprint(f\"    📊 gnn_graph_structure.png  ← unique to GNN!\")\nprint(f\"    📊 gnn_roc_curves.png\")\nprint(f\"    📊 gnn_roc_per_class.png\")\n \n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-16T07:00:50.545148Z","iopub.execute_input":"2026-05-16T07:00:50.545508Z","iopub.status.idle":"2026-05-16T07:00:50.561594Z"}},"outputs":[{"name":"stdout","text":"\n  Saved:\n    📄 gnn_results.json\n    💾 gnn_weights.pth\n    📊 gnn_training_curves.png\n    📊 gnn_confusion_matrix.png\n    📊 gnn_per_class.png\n    📊 gnn_graph_structure.png  ← unique to GNN!\n    📊 gnn_roc_curves.png\n    📊 gnn_roc_per_class.png\n","output_type":"stream"}],"execution_count":null}]}