{ "cells": [ { "cell_type": "markdown", "id": "f97f57c5", "metadata": { "id": "f97f57c5" }, "source": [ "## Environment Setup" ] }, { "cell_type": "code", "execution_count": null, "id": "4383a3fd", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:39.612144Z", "iopub.status.busy": "2026-09-08T06:37:39.611954Z", "iopub.status.idle": "2026-09-08T06:37:43.373170Z", "shell.execute_reply": "2026-09-08T06:37:43.371641Z" }, "id": "4383a3fd", "outputId": "bcce69c4-bb1f-45c1-962a-84fca621049e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1788849461.199578 4967 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1788849463.092535 4967 port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "python : 3.11.15\n", "numpy : 2.4.4\n", "pandas : 3.0.2\n", "scikit-learn: 1.8.0\n", "tensorflow : 2.21.0\n", "seed : 42\n" ] } ], "source": [ "import os, sys, io, time, json, random, warnings, urllib.request\n", "os.environ.setdefault(\"TF_CPP_MIN_LOG_LEVEL\", \"3\")\n", "os.environ.setdefault(\"PYTHONHASHSEED\", \"42\")\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import (accuracy_score, precision_score, recall_score, f1_score,\n", " confusion_matrix, roc_auc_score, roc_curve,\n", " classification_report, precision_recall_curve)\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "RANDOM_STATE = 42\n", "random.seed(RANDOM_STATE)\n", "np.random.seed(RANDOM_STATE)\n", "tf.random.set_seed(RANDOM_STATE)\n", "keras.utils.set_random_seed(RANDOM_STATE)\n", "\n", "sns.set_theme(style=\"whitegrid\")\n", "plt.rcParams[\"figure.dpi\"] = 110\n", "pd.set_option(\"display.width\", 160)\n", "pd.set_option(\"display.max_columns\", 60)\n", "\n", "print(\"python :\", sys.version.split()[0])\n", "print(\"numpy :\", np.__version__)\n", "print(\"pandas :\", pd.__version__)\n", "import sklearn; print(\"scikit-learn:\", sklearn.__version__)\n", "print(\"tensorflow :\", tf.__version__)\n", "print(\"seed :\", RANDOM_STATE)" ] }, { "cell_type": "markdown", "id": "ce00210f", "metadata": { "id": "ce00210f" }, "source": [ "## Data Loading\n" ] }, { "cell_type": "code", "execution_count": null, "id": "55cb144a", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:43.379978Z", "iopub.status.busy": "2026-09-08T06:37:43.379487Z", "iopub.status.idle": "2026-09-08T06:37:44.460883Z", "shell.execute_reply": "2026-09-08T06:37:44.460301Z" }, "id": "55cb144a", "outputId": "60e132d7-0c67-4ff8-9ecd-7379ac88a42f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "UNSW_NB15_training-set.csv -> data/UNSW_NB15_training-set.csv\n", "UNSW_NB15_testing-set.csv -> data/UNSW_NB15_testing-set.csv\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "training partition: (175341, 45)\n", "testing partition : (82332, 45)\n" ] } ], "source": [ "TRAIN_FILE = \"UNSW_NB15_training-set.csv\"\n", "TEST_FILE = \"UNSW_NB15_testing-set.csv\"\n", "\n", "MIRROR = (\"https://raw.githubusercontent.com/Nir-J/ML-Projects/master/\"\n", " \"UNSW-Network_Packet_Classification/\")\n", "\n", "SEARCH_DIRS = [\".\", \"/content\", \"/content/data\", \"/content/sample_data\",\n", " \"/content/drive/MyDrive\", \"/content/drive/MyDrive/UNSW-NB15\", \"data\"]\n", "\n", "\n", "def _find(fname):\n", " for d in SEARCH_DIRS:\n", " p = os.path.join(d, fname)\n", " if os.path.exists(p):\n", " return p\n", " for root, _, files in os.walk(\"/content\" if os.path.isdir(\"/content\") else \".\"):\n", " if fname in files:\n", " return os.path.join(root, fname)\n", " return None\n", "\n", "\n", "def _download(fname):\n", " try:\n", " urllib.request.urlretrieve(MIRROR + fname, fname)\n", " return fname if os.path.exists(fname) else None\n", " except Exception as exc:\n", " print(\" download failed:\", exc)\n", " return None\n", "\n", "\n", "def _upload(fname):\n", " try:\n", " from google.colab import files\n", " except ImportError:\n", " return None\n", " print(f\" please upload {fname}\")\n", " up = files.upload()\n", " for k in up:\n", " if k.endswith(\".csv\"):\n", " os.rename(k, fname)\n", " return fname\n", " return None\n", "\n", "\n", "def resolve(fname):\n", " for step in (_find, _download, _upload):\n", " p = step(fname)\n", " if p:\n", " print(f\"{fname:32s} -> {p}\")\n", " return p\n", " raise FileNotFoundError(\n", " f\"{fname} not found. Upload it to the runtime or place it beside this notebook.\")\n", "\n", "\n", "train_path = resolve(TRAIN_FILE)\n", "test_path = resolve(TEST_FILE)\n", "\n", "train_raw = pd.read_csv(train_path, encoding=\"utf-8-sig\")\n", "test_raw = pd.read_csv(test_path, encoding=\"utf-8-sig\")\n", "\n", "print(\"\\ntraining partition:\", train_raw.shape)\n", "print(\"testing partition :\", test_raw.shape)" ] }, { "cell_type": "markdown", "id": "a6c7299c", "metadata": { "id": "a6c7299c" }, "source": [ "## Data Understanding\n", "\n", "The dataset structure is inspected before any assumption is made about the target, the feature types or the class balance." ] }, { "cell_type": "code", "execution_count": null, "id": "6aae58ed", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:44.463523Z", "iopub.status.busy": "2026-09-08T06:37:44.463168Z", "iopub.status.idle": "2026-09-08T06:37:44.491531Z", "shell.execute_reply": "2026-09-08T06:37:44.490517Z" }, "id": "6aae58ed", "outputId": "a4bb6dfd-6fd9-4a5b-ee1e-fda48d0782a8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "columns: 45\n", "['id', 'dur', 'proto', 'service', 'state', 'spkts', 'dpkts', 'sbytes', 'dbytes', 'rate', 'sttl', 'dttl', 'sload', 'dload', 'sloss', 'dloss', 'sinpkt', 'dinpkt', 'sjit', 'djit', 'swin', 'stcpb', 'dtcpb', 'dwin', 'tcprtt', 'synack', 'ackdat', 'smean', 'dmean', 'trans_depth', 'response_body_len', 'ct_srv_src', 'ct_state_ttl', 'ct_dst_ltm', 'ct_src_dport_ltm', 'ct_dst_sport_ltm', 'ct_dst_src_ltm', 'is_ftp_login', 'ct_ftp_cmd', 'ct_flw_http_mthd', 'ct_src_ltm', 'ct_srv_dst', 'is_sm_ips_ports', 'attack_cat', 'label']\n", "\n", "int64 30\n", "float64 11\n", "str 4\n" ] }, { "data": { "text/html": [ "
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iddurprotoservicestatespktsdpktssbytesdbytesratesttldttlsloaddloadslossdlosssinpktdinpktsjitdjitswinstcpbdtcpbdwintcprttsynackackdatsmeandmeantrans_depthresponse_body_lenct_srv_srcct_state_ttlct_dst_ltmct_src_dport_ltmct_dst_sport_ltmct_dst_src_ltmis_ftp_loginct_ftp_cmdct_flw_http_mthdct_src_ltmct_srv_dstis_sm_ips_portsattack_catlabel
010.121478tcp-FIN6425817274.08749025225414158.9423808495.3652340024.2956008.37500030.17754711.83060425562177269222025336312550.0000000.0000000.000000434300101111000110Normal0
120.649902tcp-FIN14387344201478.473372622528395.112305503571.31250021749.91500015.43286561.4269341387.778330255141788414630773879712550.0000000.0000000.000000521106004311112000160Normal0
231.623129tcp-FIN8163641318614.170161622521572.27185160929.23047016231.875571102.73720317179.58686011420.926230255211615070729631149732550.1118970.0614580.0504394682400712113000260Normal0
341.681642tcpftpFIN121262877013.677108622522740.1789553358.62207013152.87654790.235726259.0801724991.784669255110711917710474428902550.0000000.0000000.000000526400112113110210Normal0
450.449454tcp-FIN10653426833.3738262542528561.4990233987.0598142147.75033375.6596022415.837634115.807000255243613754919771541902550.1283810.0711470.057234534500431221400002390Normal0
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" ], "text/plain": [ " id dur proto service state spkts dpkts sbytes dbytes rate sttl dttl sload dload sloss dloss sinpkt dinpkt \\\n", "0 1 0.121478 tcp - FIN 6 4 258 172 74.087490 252 254 14158.942380 8495.365234 0 0 24.295600 8.375000 \n", "1 2 0.649902 tcp - FIN 14 38 734 42014 78.473372 62 252 8395.112305 503571.312500 2 17 49.915000 15.432865 \n", "2 3 1.623129 tcp - FIN 8 16 364 13186 14.170161 62 252 1572.271851 60929.230470 1 6 231.875571 102.737203 \n", "3 4 1.681642 tcp ftp FIN 12 12 628 770 13.677108 62 252 2740.178955 3358.622070 1 3 152.876547 90.235726 \n", "4 5 0.449454 tcp - FIN 10 6 534 268 33.373826 254 252 8561.499023 3987.059814 2 1 47.750333 75.659602 \n", "\n", " sjit djit swin stcpb dtcpb dwin tcprtt synack ackdat smean dmean trans_depth response_body_len ct_srv_src \\\n", "0 30.177547 11.830604 255 621772692 2202533631 255 0.000000 0.000000 0.000000 43 43 0 0 1 \n", "1 61.426934 1387.778330 255 1417884146 3077387971 255 0.000000 0.000000 0.000000 52 1106 0 0 43 \n", "2 17179.586860 11420.926230 255 2116150707 2963114973 255 0.111897 0.061458 0.050439 46 824 0 0 7 \n", "3 259.080172 4991.784669 255 1107119177 1047442890 255 0.000000 0.000000 0.000000 52 64 0 0 1 \n", "4 2415.837634 115.807000 255 2436137549 1977154190 255 0.128381 0.071147 0.057234 53 45 0 0 43 \n", "\n", " ct_state_ttl ct_dst_ltm ct_src_dport_ltm ct_dst_sport_ltm ct_dst_src_ltm is_ftp_login ct_ftp_cmd ct_flw_http_mthd ct_src_ltm ct_srv_dst \\\n", "0 0 1 1 1 1 0 0 0 1 1 \n", "1 1 1 1 1 2 0 0 0 1 6 \n", "2 1 2 1 1 3 0 0 0 2 6 \n", "3 1 2 1 1 3 1 1 0 2 1 \n", "4 1 2 2 1 40 0 0 0 2 39 \n", "\n", " is_sm_ips_ports attack_cat label \n", "0 0 Normal 0 \n", "1 0 Normal 0 \n", "2 0 Normal 0 \n", "3 0 Normal 0 \n", "4 0 Normal 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"columns:\", len(train_raw.columns))\n", "print(list(train_raw.columns))\n", "print()\n", "print(train_raw.dtypes.value_counts().to_string())\n", "train_raw.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "44ae55b3", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:44.494452Z", "iopub.status.busy": "2026-09-08T06:37:44.494168Z", "iopub.status.idle": "2026-09-08T06:37:44.686371Z", "shell.execute_reply": "2026-09-08T06:37:44.684987Z" }, "id": "44ae55b3", "outputId": "7c202e28-8d80-4edd-90c5-ea8d2fe6cb0f" }, "outputs": [ { "data": { "text/html": [ "
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dtypen_uniquen_missingminmax
idint6417534101175341
durfloat647403900.059.999989
protostr1330
servicestr130
statestr90
spktsint64480019616
dpktsint644430010974
sbytesint64721402812965233
dbytesint6466600014655550
ratefloat647699100.01000000.003
sttlint641100255
dttlint64600254
sloadfloat648088500.05988000256.0
dloadfloat647747400.022422730.0
slossint64409004803
dlossint64370005484
sinpktfloat647616100.084371.496
dinpktfloat647424500.056716.824
sjitfloat647753200.01460480.016
djitfloat647683100.0289388.2697
swinint641300255
stcpbint6475265004294958913
dtcpbint6475089004294881924
dwinint64700255
tcprttfloat644331900.02.518893
synackfloat644014200.02.100352
ackdatfloat643770800.01.520884
smeanint6413570281504
dmeanint641328001458
trans_depthint641100172
response_body_lenint642386006558056
ct_srv_srcint64520163
ct_state_ttlint645006
ct_dst_ltmint64500151
ct_src_dport_ltmint64470151
ct_dst_sport_ltmint64320146
ct_dst_src_ltmint64540165
is_ftp_loginint644004
ct_ftp_cmdint644004
ct_flw_http_mthdint64110030
ct_src_ltmint64500160
ct_srv_dstint64520162
is_sm_ips_portsint642001
attack_catstr100
labelint642001
\n", "
" ], "text/plain": [ " dtype n_unique n_missing min max\n", "id int64 175341 0 1 175341\n", "dur float64 74039 0 0.0 59.999989\n", "proto str 133 0 \n", "service str 13 0 \n", "state str 9 0 \n", "spkts int64 480 0 1 9616\n", "dpkts int64 443 0 0 10974\n", "sbytes int64 7214 0 28 12965233\n", "dbytes int64 6660 0 0 14655550\n", "rate float64 76991 0 0.0 1000000.003\n", "sttl int64 11 0 0 255\n", "dttl int64 6 0 0 254\n", "sload float64 80885 0 0.0 5988000256.0\n", "dload float64 77474 0 0.0 22422730.0\n", "sloss int64 409 0 0 4803\n", "dloss int64 370 0 0 5484\n", "sinpkt float64 76161 0 0.0 84371.496\n", "dinpkt float64 74245 0 0.0 56716.824\n", "sjit float64 77532 0 0.0 1460480.016\n", "djit float64 76831 0 0.0 289388.2697\n", "swin int64 13 0 0 255\n", "stcpb int64 75265 0 0 4294958913\n", "dtcpb int64 75089 0 0 4294881924\n", "dwin int64 7 0 0 255\n", "tcprtt float64 43319 0 0.0 2.518893\n", "synack float64 40142 0 0.0 2.100352\n", "ackdat float64 37708 0 0.0 1.520884\n", "smean int64 1357 0 28 1504\n", "dmean int64 1328 0 0 1458\n", "trans_depth int64 11 0 0 172\n", "response_body_len int64 2386 0 0 6558056\n", "ct_srv_src int64 52 0 1 63\n", "ct_state_ttl int64 5 0 0 6\n", "ct_dst_ltm int64 50 0 1 51\n", "ct_src_dport_ltm int64 47 0 1 51\n", "ct_dst_sport_ltm int64 32 0 1 46\n", "ct_dst_src_ltm int64 54 0 1 65\n", "is_ftp_login int64 4 0 0 4\n", "ct_ftp_cmd int64 4 0 0 4\n", "ct_flw_http_mthd int64 11 0 0 30\n", "ct_src_ltm int64 50 0 1 60\n", "ct_srv_dst int64 52 0 1 62\n", "is_sm_ips_ports int64 2 0 0 1\n", "attack_cat str 10 0 \n", "label int64 2 0 0 1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "info = pd.DataFrame({\n", " \"dtype\": train_raw.dtypes.astype(str),\n", " \"n_unique\": train_raw.nunique(),\n", " \"n_missing\": train_raw.isna().sum(),\n", " \"min\": train_raw.apply(lambda s: s.min() if pd.api.types.is_numeric_dtype(s) else \"\"),\n", " \"max\": train_raw.apply(lambda s: s.max() if pd.api.types.is_numeric_dtype(s) else \"\"),\n", "})\n", "info" ] }, { "cell_type": "markdown", "id": "a1ca323c", "metadata": { "id": "a1ca323c" }, "source": [ "### Target Variable\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2dbf67ba", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:44.689309Z", "iopub.status.busy": "2026-09-08T06:37:44.689032Z", "iopub.status.idle": "2026-09-08T06:37:44.811191Z", "shell.execute_reply": "2026-09-08T06:37:44.809396Z" }, "id": "2dbf67ba", "outputId": "24a82c62-3791-48b8-c514-22db534cb863" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "label values : [np.int64(0), np.int64(1)]\n", "attack_cat values : ['Analysis', 'Backdoor', 'DoS', 'Exploits', 'Fuzzers', 'Generic', 'Normal', 'Reconnaissance', 'Shellcode', 'Worms']\n", "\n", "attack_cat == Normal False True \n", "label \n", "0 0 56000\n", "1 119341 0\n", "\n", "id ranges train: 1 - 175341 | test: 1 - 82332\n", "id overlap between partitions: 82332\n", "\n", "target : label (0 = normal, 1 = malicious)\n", "features : 42 (39 numerical, 3 categorical)\n", "categorical : ['proto', 'service', 'state']\n" ] } ], "source": [ "print(\"label values :\", sorted(train_raw['label'].unique()))\n", "print(\"attack_cat values :\", sorted(train_raw['attack_cat'].unique()))\n", "print()\n", "print(pd.crosstab(train_raw['label'], train_raw['attack_cat'] == 'Normal',\n", " rownames=['label'], colnames=['attack_cat == Normal']))\n", "print()\n", "print(\"id ranges train:\", train_raw['id'].min(), \"-\", train_raw['id'].max(),\n", " \"| test:\", test_raw['id'].min(), \"-\", test_raw['id'].max())\n", "print(\"id overlap between partitions:\", len(set(train_raw['id']) & set(test_raw['id'])))\n", "\n", "TARGET = \"label\"\n", "DROP_COLS = [\"id\", \"attack_cat\"]\n", "FEATURES = [c for c in train_raw.columns if c not in DROP_COLS + [TARGET]]\n", "CAT_COLS = [c for c in FEATURES if train_raw[c].dtype == object or str(train_raw[c].dtype) == \"str\"]\n", "NUM_COLS = [c for c in FEATURES if c not in CAT_COLS]\n", "\n", "print()\n", "print(f\"target : {TARGET} (0 = normal, 1 = malicious)\")\n", "print(f\"features : {len(FEATURES)} ({len(NUM_COLS)} numerical, {len(CAT_COLS)} categorical)\")\n", "print(f\"categorical : {CAT_COLS}\")" ] }, { "cell_type": "markdown", "id": "f50887d9", "metadata": { "id": "f50887d9" }, "source": [ "### Class Distribution" ] }, { "cell_type": "code", "execution_count": null, "id": "0b564c15", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:44.813893Z", "iopub.status.busy": "2026-09-08T06:37:44.813656Z", "iopub.status.idle": "2026-09-08T06:37:44.841410Z", "shell.execute_reply": "2026-09-08T06:37:44.840222Z" }, "id": "0b564c15", "outputId": "55418a8e-6063-479a-e497-ac7608e22bf9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " partition normal malicious total malicious_% ratio_mal_to_norm\n", "training-set 56000 119341 175341 68.06 2.131\n", " testing-set 37000 45332 82332 55.06 1.225\n", "\n", "attack categories (training partition):\n", "attack_cat\n", "Normal 56000\n", "Generic 40000\n", "Exploits 33393\n", "Fuzzers 18184\n", "DoS 12264\n", "Reconnaissance 10491\n", "Analysis 2000\n", "Backdoor 1746\n", "Shellcode 1133\n", "Worms 130\n" ] } ], "source": [ "def dist(df, name):\n", " vc = df[TARGET].value_counts().sort_index()\n", " return pd.Series({\"partition\": name, \"normal\": vc.get(0, 0), \"malicious\": vc.get(1, 0),\n", " \"total\": len(df),\n", " \"malicious_%\": round(100 * vc.get(1, 0) / len(df), 2),\n", " \"ratio_mal_to_norm\": round(vc.get(1, 0) / vc.get(0, 1), 3)})\n", "\n", "class_table = pd.DataFrame([dist(train_raw, \"training-set\"), dist(test_raw, \"testing-set\")])\n", "print(class_table.to_string(index=False))\n", "print()\n", "print(\"attack categories (training partition):\")\n", "print(train_raw['attack_cat'].value_counts().to_string())" ] }, { "cell_type": "markdown", "id": "6da6e33c", "metadata": { "id": "6da6e33c" }, "source": [ "### Data Quality Checks" ] }, { "cell_type": "code", "execution_count": null, "id": "00402eeb", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:37:44.844027Z", "iopub.status.busy": "2026-09-08T06:37:44.843801Z", "iopub.status.idle": "2026-09-08T06:38:13.981965Z", "shell.execute_reply": "2026-09-08T06:38:13.980741Z" }, "id": "00402eeb", "outputId": "6e6d5892-b384-43af-fc90-c8780a8c16e1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "missing values train: 0 | test: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "infinite values train: 0\n", "negative values : none\n", "constant columns: none\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "exact duplicate records (ignoring id) train: 74072 (42.2%) test: 28380 (34.5%)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "testing records whose feature vector also occurs in training: 8541 (10.4%)\n", "feature-identical training groups carrying conflicting labels: 229\n" ] } ], "source": [ "print(\"missing values train:\", int(train_raw.isna().sum().sum()),\n", " \"| test:\", int(test_raw.isna().sum().sum()))\n", "print(\"infinite values train:\", int(np.isinf(train_raw[NUM_COLS].to_numpy(dtype=float)).sum()))\n", "neg = {c: int((train_raw[c] < 0).sum()) for c in NUM_COLS if (train_raw[c] < 0).any()}\n", "print(\"negative values :\", neg if neg else \"none\")\n", "print(\"constant columns:\", [c for c in FEATURES if train_raw[c].nunique() <= 1] or \"none\")\n", "\n", "dup_train = int(train_raw.duplicated(subset=FEATURES + [TARGET]).sum())\n", "dup_test = int(test_raw.duplicated(subset=FEATURES + [TARGET]).sum())\n", "print()\n", "print(f\"exact duplicate records (ignoring id) train: {dup_train} ({dup_train/len(train_raw):.1%})\"\n", " f\" test: {dup_test} ({dup_test/len(test_raw):.1%})\")\n", "\n", "key_tr = train_raw[FEATURES].astype(str).agg(\"|\".join, axis=1)\n", "key_te = test_raw[FEATURES].astype(str).agg(\"|\".join, axis=1)\n", "overlap = int(key_te.isin(set(key_tr)).sum())\n", "print(f\"testing records whose feature vector also occurs in training: {overlap} ({overlap/len(test_raw):.1%})\")\n", "\n", "conflict = int((train_raw.groupby(key_tr)[TARGET].nunique() > 1).sum())\n", "print(f\"feature-identical training groups carrying conflicting labels: {conflict}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "8a4e0a90", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:13.984985Z", "iopub.status.busy": "2026-09-08T06:38:13.984743Z", "iopub.status.idle": "2026-09-08T06:38:14.153619Z", "shell.execute_reply": "2026-09-08T06:38:14.151743Z" }, "id": "8a4e0a90", "outputId": "ec238c54-eb37-4c87-a270-a00972ddb64c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "proto train levels: 133 test levels: 131 unseen in test: []\n", " top: {'tcp': 79946, 'udp': 63283, 'unas': 12084, 'arp': 2859, 'ospf': 2595}\n", "service train levels: 13 test levels: 13 unseen in test: []\n", " top: {'-': 94168, 'dns': 47294, 'http': 18724, 'smtp': 5058, 'ftp-data': 3995}\n", "state train levels: 9 test levels: 7 unseen in test: ['ACC', 'CLO']\n", " top: {'INT': 82275, 'FIN': 77825, 'CON': 13152, 'REQ': 1991, 'RST': 83}\n" ] } ], "source": [ "for c in CAT_COLS:\n", " tr_lv, te_lv = set(train_raw[c]), set(test_raw[c])\n", " print(f\"{c:8s} train levels: {len(tr_lv):3d} test levels: {len(te_lv):3d} \"\n", " f\"unseen in test: {sorted(te_lv - tr_lv)}\")\n", " print(f\" top: {train_raw[c].value_counts().head(5).to_dict()}\")" ] }, { "cell_type": "markdown", "id": "856dfcf7", "metadata": { "id": "856dfcf7" }, "source": [ "The `service` column uses `-` as an explicit placeholder for *no application-layer service identified*. It is a legitimate category rather than a missing value and is kept as its own level. The `state` column contains levels in the testing partition that do not occur in the training partition, so the encoder must tolerate unseen categories at inference time." ] }, { "cell_type": "code", "execution_count": null, "id": "79c284ed", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:14.156690Z", "iopub.status.busy": "2026-09-08T06:38:14.156459Z", "iopub.status.idle": "2026-09-08T06:38:14.518167Z", "shell.execute_reply": "2026-09-08T06:38:14.517417Z" }, "id": "79c284ed", "outputId": "c8cbe8f3-59ff-400b-b2fd-68fe0ff7faa1" }, "outputs": [ { "data": { "image/png": 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Gxvj4eKtzUhrn/fvcB/tO7zjz1KlTxtKlSxtbtmxpMda9deuWMSAgwFimTBnj2bNnzeXp/Q7TpGzZssbu3buneOxp0RJkIs+B3r17p1humgljNBqJj48nLi6Ol19+meLFi7N///40tW1vb0/37t0tyvz9/QE4ffp0mtqoUKGCVRbc9LTJg22sXbuWXLlyWT1x06hRI4oXL56mvkxy5sxpNRW4UaNGlCxZkrVr12IwGID7a3+anrI0GAzcuHGDuLg4SpcujZubGwcOHLBqu3Tp0tSuXTtd8WSGuLg4du/ezeuvv25+WsCkZ8+euLi4sGbNGqvzQkJCLJ4OcnFxoWLFipw6deqJxywiIvK4Vq5cidFopF27dsTFxVn8qV+/PgaDgW3btgHw0ksvAfeXJvn3MhOZQWOZzKGxjIjI0/Prr78SGxtL27ZtzWUlSpSgYsWKREVFmWcOZJTpeweApKQkrl27RlxcHLVq1eLevXscPHjQfHzFihW4uLjw/vvvWyw/BaS4kfmdO3d47733iIyM5OOPP2bo0KFW56Xk6NGjHD9+nBYtWuDn52d1/MG+0hP/w6Rn3LJlyxYSEhLo3LmzxRP5Tk5OVt+/AObPxrCwMIvy4sWLExAQwOnTp62Wmk/PZ/2KFSvInj07zZo1s4q9YcOGxMfH83//938AuLm5ARAdHc3du3fT1P6D4uLiAKxm56ZVnTp18PLysirPnj27+ed69+5d88/RNF5LaTyUkoYNG1K4cGGLstdff527d++al1h9lLSMWw4dOsTff/9Nq1atyJcvn7nczs4u1e/4HqZly5YULFjQ/Nre3p633noLwGJs9Tjv9+Dg4Efu+fIkrFu3DoPBQO/evS1mR7u4uNCzZ0/u3bvH+vXrLc55nO8wc+XKleKywk+TliATecZ5eHiQK1euFI/9/vvvTJ48mX379ll9GVGoUKE0tZ8vXz6rjahy584NkKY1VwGrDy/434ftg22cPXsWLy+vFDdsLVGiRLr+J7tIkSIpTkl+9dVXOXnyJHFxceapjuvWrWPmzJkcPHjQagCR0jUWK1YszXFkprNnzwKkONjIkSMHhQsXtloXHlK//2n9+YmIiNjCyZMnAWjVqlWqdS5fvgxA8+bNWbVqFTNmzCAyMhI/Pz+qVKnCG2+8keLnZnppLJM5NJYREXl6fvrpJ7Jnz06pUqX466+/zOU1a9YkPDycHTt2UKNGjQz3c/v2baZMmcLPP//M+fPnrY5fv37d/O/Tp09TokQJiy+BH2b8+PHEx8czatQogoKC0hyT6fO2bNmyj6ybnvgfJj3jFtMX+SVKlLCqY1ra7UFnz57F3d3d4ot6E9NePWfOnKF06dLm8vR81p88eZLExMSHJmxMsYeEhLBx40Y+++wzvvrqKypUqEDVqlUJCAhI8fM6NY+bAEztuu7du8esWbNYunQpp0+fNj+oYpLZ3189Thv/HjNC2t8Dj5LSOaayB//7f5z3e3ofIsosDxs3mspMdUwe5ztMo9GYpsTuk6QEjMgz7t9rZJscPHiQ7t27U6hQIQYMGEChQoXIkSMHdnZ2fP7559y+fTtN7WfLli3VY2n9wMyMNp6UdevW0a9fP3x8fBg2bBgFChQge/bsAAwYMCDF+FK758+qlJ4kEhERedaZ/sd5+vTpqa7zbvofXCcnJ2bOnMmhQ4fYsmULe/bsYc6cOUyfPp3BgwfTo0ePDMWisYxtaSwjIpI+sbGxbN68mXv37hEQEJBinaioqExJwAwaNIj169cTFBRElSpVyJ07Nw4ODhw8eJCvvvrK6ovw9GjYsCFr1qwhIiKC2rVrm/c/y0yZFX96xi1PQ3o+6w0GA25ubkyaNCnVOq+++ipwP5GwaNEi9u7dy/bt29m9ezeTJ09m8uTJjBs3jubNmz+0L9M+cY/7EEVq1zV27FjmzJlDkyZN6N27Ny+//DKOjo7ExsYydOjQp/r91bM8bnmc97tpXPk8eJyf340bNyhTpsyTCilNlIAReU6tXLmS5ORkZs6cafUhf+3aNauM8LOgcOHCnD17luTkZBwcLH/9xMTEpKutM2fOkJSUZDXw+fPPP3F1dTV/6C9btgxnZ2fmzZtn8UGekJDAjRs30tXnk86Ym36OJ06csDqWmJjI2bNnKVq06BONQURE5GkpVqwYv/32G3nz5qVcuXJpOqdcuXLmutevX6djx45MmDDBvBTE0/is1lgmdRrLiIg8HUuWLOHevXsMGzYMT09Pq+NRUVGsWbOGq1evmp8Ofxw3b95k/fr1tGzZks8++8ziWErL/RQrVozTp0+TkJCQplkw1apVo3379vTp04eQkBAiIyMtlllKjemJ/SNHjmRq/A/7nEzPuMW0IklMTAx169a1OGaaSfOgIkWKEBMTw+XLl602LTctPVakSJGH9vkwxYoVIyYmhjJlyqTp/WBvb0+VKlWoUqUKAP/88w9t2rRh/Pjxj0zAlCpVCrh/fytUqGBxLCPjkGXLllGlShWrJNLmzZsfu80nyTQmSml8mNJ74FFSOsdUZhpbpff9/ijp/Xmlt77pPf3nn39azYIxve8zmtQ0jdszY8Z8Rjy7KTsReShTxv3fGd4FCxaYp44+axo2bMj169f58ccfLcrXrl2b7jW+b926xdy5c63aOXnyJA0bNjTfH3t7e+zs7Kyy/FOmTEn3kzqmAWRapyjD/fXd0/rkh4eHB1WqVGHLli1W65fOnj2bhIQEGjdunOa+RUREnmWmJTwmTJiQ4hrjN2/eJCkpCfjfeuIPypUrF4ULF+bu3bvcunULeLzP6vTQWObhNJYREXnyjEYjUVFRFChQgG7dutG0aVOrP8HBwSQlJbF8+XLzeS4uLqn+Ps+ZMyfXr1+3+n7B9IXqv8vj4+OJjIy0aqdly5YkJCTwzTffWB1L7TOrcuXKREZGcuPGDYKDg9P0RXHp0qXx8vJixYoV/PHHH6n2ld74H/Y5mZ5xS82aNcmRIwfz588nPj7eXCcpKSnFfhs1agTc/2x/0F9//cWqVasoVqyYeSmyx9G6dWsAvvzyyxRnCTz4HVJKY64CBQqQJ08erl69+si+qlatCsC+ffusjmVknGZvb28V+927d5kxY0a623oaypUrR4ECBVi+fDkXL140lxuNRmbOnJnu9lasWGGxpJjBYGD69OnA/94/6X2/P4rp55XWcWB6f76m8e6sWbO4c+eOufz27dvMmjWLbNmy0aBBg/QF/S+m92G1atUy1E5GaQaMyHOqcePGREZG0rt3b9q3b0/27NnZu3cvW7ZsoUiRIty7d8/WIVrp1asXP//8M6NGjeLw4cP4+Phw8uRJFi9eTOnSpTl69Gia2ypSpAjTp0/nzz//xM/Pj5MnT7Jw4UI8PDx47733zPWaNm3K6tWr6dKlC23atMFoNLJlyxb+/PPPdD8J9Oqrr5IzZ07mz59P9uzZeemll/Dw8HjotO7y5csTFRXF119/TcmSJbG3t6devXqpPg00fPhwgoOD6datGx07dqRw4cLs2bOHVatWUbp0ad588810xSwiIvKs8vX15b333uPrr78mICCAgIAAPD09uXLlCsePH2f9+vX8/PPPFCpUiKlTp/Lbb79Rr149ChUqRLZs2fj999/ZvHkz9erVM3+mP85ndXpoLKOxjIiIrW3bto1z587RvXv3VJ84f/3113FzcyMqKsq8YXWFChXYvn07M2bM4JVXXsHOzo433ngDuP+7fuPGjXz66adUrFiRbNmyUb16dV5++WVq1arFypUrcXJyonz58ly8eJHFixfz8ssvW/XbtWtXNm3aRGRkJIcPH6Z27dq4urpy+vRptm7dyqpVq1KM19fXl++//54ePXoQEhJCRESEeSZFSuzs7BgzZgzdunWjU6dOtG3bltKlS3P79m3+7//+j8KFC/PBBx/g6uqarvgf9jmZnnHLSy+9xPvvv8/nn39OUFAQbdq0wdHRkVWrVpkfsHjwZ9e6dWtWrFjBDz/8wN9//02tWrW4dOkSCxYswGg0MnLkyAzNHmnSpAnt27fnp59+4ujRozRs2JC8efMSGxvLoUOH+PXXXzl06BAAH3/8Mf/88w+vv/46BQsW5N69e2zcuJETJ04QEhLyyL48PDyoXr06v/76q9XeGxkZpzVt2pQFCxbwzjvv8Prrr3P9+nVWrlz5TK7+AveXyho+fDhhYWG0bduWDh06kCtXLtatW0dCQgKQvhkjJUqUoH379nTq1Mnczo4dO3jjjTeoXr06QLrf749Svnx55s2bx8iRI6lTpw6Ojo74+fmlOislvT/fokWLEhoaypQpU2jfvj0tWrTAaDSyYsUKjh8/bt5uISM2bdqEu7s7/v7+GWono5SAEXlOVaxY0bwOZ3h4OE5OTlSqVIkffviBkSNHprjZlq299NJLzJ8/ny+//JI1a9bw888/U7ZsWb777jvmzJmTrimRnp6ehIeHM27cOMaNG4ednR21a9dmyJAhFChQwFyvefPmJCQkMGfOHL788kty5syJv78/8+fPp3PnzumKP3v27EycOJGvv/6a0aNHk5SURNWqVR86WBgwYADXr19n/vz53LhxA6PRyPr161P90qJMmTIsWrSI8PBwli5dSnx8PPny5ePNN9+kX79+z92a7iIiIg/Tt29ffHx8mDt3LvPmzePWrVvkzp2b4sWL895775E3b17g/hNyly9fZvXq1Vy5cgUHBwcKFizIoEGD6NKli7m9x/msTg+NZTSWERGxtZ9++gm4/6V6apycnKhfvz7Lly9n3759VKxYkU8++YRPP/2UadOmmWeOmhIw3bt35+zZs6xevZqFCxdiMBj4/vvvefnll/nyyy+ZMGECmzZtYsWKFRQsWJDg4GDKlStnTu6YODo6MmvWLObMmcOKFSsIDw/HwcGBQoUK0bRp04del7e3N/PmzaN79+506dKF2bNnU7Zs2VTr+/j4sGTJEqZOncr69etZvHgxuXLlonTp0tSsWdNcLz3xP+pzMq3jFrifjHJzc2PWrFlMmjSJ3LlzExAQQLNmzQgKCrJIHDg4OPDdd98xY8YMVq1axZYtW8iRIweVK1fm7bffxs/P76H3Li0+++wzqlWrxo8//sjs2bNJTEwkT548lCpViuHDh5vrtWrVimXLlrFy5UquXLlCjhw5KFq0KCNHjqR9+/Zp6is4OJiwsDB27NhhMcbIyDht6NChuLq68t///pcNGzaQL18+AgICaNWq1SOXRbOVhg0bMm3aNMLDw5k+fTqurq40aNCAvn37Ur9+/XQlj0JCQrh9+zZz587l3Llz5MmTh7fffpu3337bol563u+PEhAQwJEjR/j555+Jjo7GYDAwZsyYVBMwj/PzfffddylWrBjz5s0jPDwcuP+74Kuvvkp1f6u0io+PZ926dQQHB9s8UWdntPWukiIi3B/4GQwGfvnll0fWrV+/PgULFrRatkNERETEVjSWERERkUf55ZdfeO+995g4ceIzmzjIKIPBQGBgIPny5XtmlwizpQMHDhAUFMTAgQPp06fPQ+vu3LmTrl27MmbMGAIDA59ShC+GGTNmMHXqVNasWWORHLUF7QEjIk/V7du3rcrWrl3Ln3/+afGUioiIiMizSGMZEREReZQ7d+5Y7cWRlJTErFmzcHR0tPmeFE+Svb09H330EZs3b05xL5is4u7duyQnJ1uUPbh3S61atWwRVpZw8+ZNZs6cSWhoqM2TL6AlyETkKQsNDSVPnjz4+Pjg5OTEwYMHWb58OXny5KF37962Dk9ERETkoTSWERERkUfZs2cPI0eOpGnTphQsWJCLFy/y888/ExMTQ79+/R5rT47nyWuvvcaxY8dsHYZN/f3333Tt2pXmzZtTrFgxrl27xrp16zhw4ACtW7emTJkytg7xheXm5sauXbtsHYaZEjAi8lTVq1eP5cuX8+uvv5KQkICHhwetWrUiLCyMfPny2To8ERERkYfSWEZEREQepXDhwnh5ebFs2TLi4uJwcHDg1VdfZfTo0bRt29bW4clT4O7uTpUqVcx7GBqNRooVK8aQIUPo1q2brcOTp0h7wIiIiIiIiIiIiIiIiGQy7QEjIiIiIiIiIiIiIiKSyZSAERERERERERERERERyWRKwIiIiIiIiIiIiIiIiGQyB1sHIBmzZ88eAOztlUsTEXlRGAwGACpXrmzjSESePo1tREReLBrXSFansY2IyIslvWMb/fYXERERERERERGRTGcwGMxfVmY1WfnaQdefla8/K197SjQD5jlneoKiYsWKNo5EREQyy759+2wdgojNaGwjIvJi0bhGsjp7e3sMBkOWHdscPXoUgNKlS9s4kqcvK1876Pqz8vW/6Nee3rGNEjAiIiIiIiIiIiJPiJ2dna1DsJkiRYrYOgSbycrXDrr+rHz9WfnaU6IEjIiIiIiIiIiIyBO0e+deW4cgIpLleZf1ws3N9an2qQSMiIiIiIiIiIjIE2IwGOnQ6k1bhyEikuX9uDySKtUqPdU+7Z9qbyIiIiIiIiIiIiIiIlmAEjAiIiIiIiIiIiIiIiKZTAkYERERERERERERERGRTPZMJmBu3brFpEmT6N27N9WrV8fb25sZM2ZY1DEYDCxZsoTQ0FDq1KlDhQoVCAgIYMqUKdy5cyfFdqOiomjevDm+vr40atSIyMhIjEajVb0bN24wYsQIqlevToUKFejSpQt//PFHim3u3buXzp07U758efz9/Rk5ciS3bt2yqmcwGPjuu+9o0KABvr6+BAQEsHz58se4OyIiIiIiIiIiIiIi8qx7JhMwV69eZfLkyRw/fpyyZcumWOf27dsMGzaMq1ev0rFjRz788EN8fX0JDw+nV69eVomVhQsX8tFHH1GyZElGjBhBpUqVGDNmDNOmTbOoZzAY6NOnDytXriQ4OJgPPviAq1ev0rVrV06ePGlR98iRI3Tv3p2EhASGDBlCUFAQixcvpn///lbxTpw4kfHjx1OjRg0+/vhjChYsyODBg1mxYkUG75aIiIiIiIiIiGS28PBwvL29bR2GiIg8xxxsHUBK8uXLx6+//kr+/Pk5d+4cDRo0sKrj6OjIggULqFSpkrmsffv2FCxYkPDwcLZs2UKtWrUASExMZOLEidSqVYvw8HAAgoKCuHfvHtOmTaNDhw54eHgAEB0dzb59+5g4cSLNmzcHoFmzZjRp0oRJkybxzTffmPubMGECbm5uzJ07Fzc3NwAKFSrE8OHD2bx5M3Xq1AEgNjaWiIgIOnbsyMiRI839h4SEMG7cOJo3b46DwzP5oxARERERERERSdWSJUsYNmwYjo6OrFmzhldeecXi+FtvvcWJEyfYsGGDjSIUERGxnWdyBoyTkxP58+d/ZJ0Hky8mjRo1ArCYrbJz506uXbtGp06dLOoGBweTmJjIpk2bzGWrV68md+7cNG3a1Fzm4eFBs2bN2LhxI4mJiQDEx8ezbds2AgICzMkXgFatWuHi4sIvv/xiLlu3bh1379616N/Ozo5OnTpx6dIl9uzZ89BrFRERERERERF5lt29e9dqlREREZGs7oWbdnH58mUA3N3dzWWHDx8GwMfHx6JuuXLlsLe35/DhwwQGBgL3lxUrW7Ys9vaWuSlfX19+/PFHYmJiKFu2LMeOHSM5OdmqTScnJ8qUKWPu09Smk5OT1bRVPz8/8/Fq1ao99jUbDAaOHj362OeLiMizxWAwWH0OiYiIiIg8y8qUKWPeq/ffs2Ayy+3bt8mRI8cTaVtERORJeOG+3Zk5cyY5c+Y0L/8FcOnSJeD+0mYPcnJywt3dnYsXL1rUzZs3r1W7pnNNdU1tplQ3b968Vm3myZMHOzs7q3pwf4kyEREREREREZHnVZ8+fQAeOQvm3r17TJ06lUaNGuHj40PdunUZN26cecURk/r169OzZ0+2b99OUFAQvr6+zJw5k3PnzuHt7c2MGTP44YcfaNCgAeXLl6d79+6cP38eo9HItGnTqFOnDn5+foSGhhIXF2fR9vr16wkNDaV27dr4+PhQr149xo4dy507dzL3poiISJb3Qs2AmTZtGtu2bWPEiBHkzp3bXJ6YmIijo6NVAgTA2dnZ4gM2MTERJycnq3qmMtOAwPR3SnXT2qazszNAhj/g7e3tKV26dIbaEBGRZ8e+fftsHYKIiIiISLq88sortG3blsWLFz90FsyIESOIioqicePGdO/enYMHDzJr1ixOnDjBjBkzLL67OXPmDO+88w5BQUG0a9eOAgUKmI/9/PPPJCUlERISwvXr15k5cybvvvsutWrVYtu2bfTq1YszZ84wd+5cvvjiC8aNG2c+d8mSJTg5OdGlSxfc3NzYv38/c+bM4cKFC0ycOPHJ3SQREclyXpgEzH//+1++/vpr2rVrR3BwsMWx7Nmzc/fu3RSXdLlz5445EWKqm5SUZNW+qSx79uwWf6dUN61tmhIvD9YVkafj7u3b3Dx71tZhyAvErXBhHLUcgoiI2IjGNpLZNLaRxxEaGsrixYuZNm0an376qdXxo0ePEhUVRWBgIGPGjDGXv/LKK3z77bds2rSJevXqmcvPnDnDlClTaNCggbns3LlzAFy4cIG1a9fy0ksvAfeX8Z0+fTqJiYksXboUR0dHAOLi4vjvf//Lp59+av4uZ/z48RZLmXXs2JGiRYvy9ddfM3jwYItEj4iISEa8EAmYrVu3MnjwYOrWrcvIkSOtjpuW+rp06RL58+c3lyclJXHt2jWLpcny5s1rXl7sQaYlxUx1H2zz3y5dumTV5rZt26wSQKZzH4xJRJ6Om2fPsmPUmEdXFEmj6sOH4eHlZeswREQki9LYRjKbxjbyOAoUKGCeBfPWW29RsGBBi+ObN28GoHv37hbl3bt3Z+rUqVYJGE9PT4vky4OaNGliTr7A//bZbdmypTn5YipftWoV//zzD8WLFwcwJ18MBgO3bt3i7t27VK5cGaPRyKFDh5SAERGRTPPc7wGzf/9++vfvj4+PD19//TUODtY5pTJlygBw8OBBi/KDBw9iMBjMxwFKly7N4cOHMRgMFnUPHDiAs7MzJUqUAMDLywsHBwerNpOSkjhy5IhFm2XKlCEpKYnjx49bxW7qU0RERERERETkeRcaGgqkvBfM+fPnsbOzMydCTNzc3MibNy/nz5+3KC9cuHCq/fw7SeLm5gbcT9qkVH7jxg1z2fHjx+nduzcVK1akSpUq1KhRg5CQEADi4+Mfen0iIiLp8VwnYE6ePEmfPn0oWLAg06dPN08l/bfq1avj7u7OggULLMoXLFiAs7OzxdMVTZs25erVq0RHR5vL4uLiiI6Opm7duuY+3NzcqFGjBqtWrbL4cF6+fDkJCQk0bdrUXNagQQMcHR0t+jcajSxcuJC8efNSuXLljN0IEREREREREZFnQIECBWjXrh1Lly61Sqik18OWbM+WLVu6yo1GIwA3b96ka9eunDx5kgEDBjB16lQiIiL44osvAKweyBUREcmIZ3YJsnnz5nHjxg1u3rwJwM6dO0lOTgagS5cu2NnZ0bNnT27cuEHPnj3ZtGmTxflFihShYsWKwP09WN555x0+/fRTwsLCqF27Nrt372bFihWEhYXh4eFhPq9JkyZUqFCBjz76iJiYGHLnzs2CBQtITk7m3XfftehjwIABdOzYkZCQEDp06EBsbCyzZ8+mevXq1K1b11zP09OTrl27MmvWLAwGA35+fqxfv57du3czduxYi6mxIiIiIiIiIiLPs9DQUKKiopg6dapFecGCBTEajZw6dQpvb29zeXx8PJcuXbL4LuVJ2blzJ1evXmXSpElUrVrVXL5169Yn3reIiGQ9z2wCZvbs2RZPSmzZsoUtW7YA99fzBPjnn38A+Oqrr6zOb9OmjTkBAxAcHIyTkxOzZ89m48aNeHp6MnToUKt1R7Nly8aMGTP48ssvmTt3LomJifj6+jJ69GhKlixpUbdcuXJERETw1VdfMWbMGFxcXAgMDGTQoEHY2dlZ1B00aBDu7u4sXLiQpUuXUrRoUcaOHUvr1q0f+x6JiIiIiIiIiDxrPD09CQoKYtGiRRbLjdWpU4cJEyYwZ84cRo8ebS6fM2cO9+7ds1ih5Ekx7c1rmhED92e9REREPPG+RUQk63lmEzAbNmx4ZJ1jx46lq82goCCCgoIeWS9XrlyMGjWKUaNGPbJulSpVrJY2S4m9vT19+vShT58+aYpVREREREREROR59dZbbxEVFcXx48cpWLAgcH8P3Hbt2hEVFUV8fDzVqlXj8OHDLF68mFq1alGnTp0nHlelSpVwd3dn6NChhISE4ODgwOrVq0lISHjifYuISNbzXO8BIyIiIiIiIiIizx7TLJh/+/TTT3nvvfc4fPgwY8aMYcuWLfTo0YNvv/3WajWRJ8Hd3Z0ZM2bg6elJeHg406dPx8vLi7Fjxz7xvkVEJOuxMz4451KeO/v27QOwWG5NRB4t7vhxdowaY+sw5AVSffgwPLy8MqUt/W6XrEzvf5HHo7GNZLbMGtvo97pkdfv27ePePQMdAt60dSgiIlnej8sjqVKtUobaSO/YRjNgREREREREREREREREMpkSMCIiIiIiIiIiIiIiIplMCRgREREREREREREREZFMpgSMiIiIiIiIiIiIiIhIJnOwdQAiIiIiIiIiIiIvKnt7O35cHmnrMEREsjzvsl5PvU8lYERERERERERERJ6gKtUq2ToEm0hISADAxcXFxpE8fVn52kHXn5WvPytfe0q0BJmIiIiIiIiIiMgTYjQabR2CzZw5c4YzZ87YOgybyMrXDrr+rHz9WfnaU6IEjIiIiIiIiIiIiIiISCZTAkZERERERERERERERCSTKQEjIiIiIiIiIiIiIiKSyRxsHYCIiIiIiIiIiMiLys7OztYh2EyRIkVsHYLNZOVrB11/kSJFuHPnjq3DkGeAEjAiIiIiIiIiIiJP0B+7/7B1CCLyFJXwLoGzs7Otw5BngBIwIiIiIiIiIiIiT4jRYOSd9u/aOgwReYom/fQNJcuWtHUY8gzQHjAiIiIiIiIiIiIiIiKZTAkYERERERERERERERGRTKYEjIiIiIiIiIiIiIiISCZTAkZERERERERERERERCSTKQEjIiIiIiIiIiJZypIlS/D29ubcuXO2DkVERF5gDrYOQEREREREREREnh9xcXFERkayceNGzp07R3JyMvny5aNy5coEBgZSvXp1W4coIiLyTFACRkRERERERERE0uSPP/7grbfe4ubNmzRv3pyOHTvi5OTEuXPn2LBhA926dWPGjBnUqVPH1qE+VKtWrXjjjTdwcnKydSgiIvICUwJGREREREREREQe6caNG/Tr1w97e3uWLVtGyZIlLY4PGDCANWvWkDNnThtF+GgJCQm4uLiQLVs2smXLZutwRETkBac9YERERESeYStWrMDb2xtfX1+rYydPnqRXr15UrFiRqlWrMnDgQC5fvpxiO1FRUTRv3hxfX18aNWpEZGQkRqPRqt6NGzcYMWIE1atXp0KFCnTp0oU//vgjxTb37t1L586dKV++PP7+/owcOZJbt25l7IJFRETkmbVw4UJiY2P58MMPrZIvJo0bN6ZKlSoWZRcvXuSjjz7i9ddfx8fHh2bNmjF//nyLOjt37sTb25tVq1Yxbdo0ateuja+vL926deOvv/6y6ufAgQP07t2bypUr4+fnR6dOndixY4dFnfDwcLy9vTl+/DgffPABVatWJSAgAEh9D5g//viD0NBQqlatSvny5QkICGD69OnpvlciIiKgGTAiIiIiz6xbt27x5Zdf4uLiQnJyssWxCxcuEBwcjKurKwMGDOD27dvMmjWL48ePExUVhbOzs7nuwoUL+eSTT2jcuDFvvvkmu3fvZsyYMdy+fZu+ffua6xkMBvr06cOxY8fo0aMHHh4eLFiwgK5duxIVFWXxRcuRI0fo3r07JUqUYMiQIcTGxhIREcHp06eJiIh48jdHREREnroNGzaQPXt2GjVqlOZzrly5QocOHbh37x6dOnXi5ZdfZvv27YwcOZJr167x9ttvW9SfOXMm9vb29OjRg/j4eGbOnMmgQYNYtGiRuc6uXbvo2bMnZcqUoV+/fjg4OLB8+XJ69uzJ7NmzqVatmkWbAwYMoFChQrz77rvcvXs31Vi3b99Onz59ePnllwkJCSFfvnycOnWKDRs28NZbb6X5mkVEREyUgBERERF5Rk2dOpWcOXNSrVo1Vq9ebXFs2rRp3Lp1i8WLF1OwYEEAfH19efPNN4mKiiI4OBiAxMREJk6cSK1atQgPDwcgKCiIe/fuMW3aNDp06ICHhwcA0dHR7Nu3j4kTJ9K8eXMAmjVrRpMmTZg0aRLffPONuf8JEybg5ubG3LlzcXNzA6BQoUIMHz6czZs3P/PrvouIiEj6xcTEULx4cRwdHS3K4+PjSUpKMr92dnY2L0P29ddfk5SUxMqVK81jjk6dOjF8+HCmT59OSEgIL730kvncO3fusHz5cvPeLC+99BKff/45x48fx8vLC6PRyIgRI6hcuTIRERHY2dkB0LFjR9q0acPEiRNZuHChRXwlSpQwj4NSYzAY+Pjjj/Hw8GDZsmW4u7ubj6U0a1hERCQttASZiIiIyDPo9OnTREZGMmzYMBwcrJ+ZWbNmDXXq1DEnXwD8/f0pVqwY0dHR5rKdO3dy7do1OnXqZHF+cHAwiYmJbNq0yVy2evVqcufOTdOmTc1lHh4eNGvWjI0bN5KYmAjc/5Jl27ZtBAQEmJMvcH8zWxcXF3755ZcMX7+IiIg8e+Lj43FxcbEq//jjj6lRo4b5z2effQbcT1ysXr3a/GBGXFyc+c/rr79OYmIi+/fvt2irdevW5uQLYF7O7OzZswAcPXqUU6dOERAQwNWrV83txcfH4+/vz/79+7l9+7ZFm/8eB6Xk0KFDnD17lq5du1okXwBzkkdERCS9NANGRERE5Bk0evRoqlWrRp06dawSGrGxsVy5cgUfHx+r8/z8/NiwYYP59eHDhwGs6pYrVw57e3sOHz5MYGAgcH9ZsbJly2Jvb/mMjq+vLz/++CMxMTGULVuWY8eOkZycbNWmk5MTZcqUMfcpIiIiL5acOXOmuN9b3759CQoKAiA0NNRcHhcXx/Xr11m8eDGLFy9Osc0rV65YvC5QoIDFa9PsmBs3bgBw6tQpAD766KNU47x27Ro5cuQwvy5cuHCqdU1MCZ5SpUo9sq6IiEhaKQEjIiIi8ozZtGkTW7duZfny5Skev3jxIgB58+a1OpY3b17i4+NJSEjAxcWFS5cuAZAvXz6Lek5OTri7u5vbArh06RIVK1a0atN07sWLFylbtqy5zdT6j4mJSctlpspgMHD06NEMtSGSlRQpUsTWIcgLKiEhgTNnzmSoDYPBYJXYl+dXyZIlOXLkCHfv3rVYhszLywsvLy8AsmXLZi43GAwABAQE0LZt2xTbfPXVVy1eP3j+g0zLgJn+HjhwYIoPowDmpc5MsmfPnuo1iYiIPElKwIiIiIg8Q5KSkhgzZgwdO3a0+kLC5M6dOwAWy3OYODs7A/f3fnFxcSExMRFHR8cUl85wdnY2t2U6J6U2TWWmJchMf6fW/4NtioiIyIujXr167Nu3j9WrVxMQEPDI+h4eHuTMmZPk5GT8/f0zJQbTbJacOXNmWpsPtnvixAlq166dae2KiEjWpgSMiIiIyDMkMjKSq1evEhYWlmodU5Llwc1uTUzJD9OTntmzZ+fu3bspPoF8584dc1umuim1aSp7sM2H9f9gm4/D3t6e0qVLZ6gNkawm0dYByAvJxcUlw7+P9+3bl0nRyLOgY8eOzJ07ly+++IIyZcpQsmTJh9bPli0bTZo0YeXKlRw9etTq/RQXF2c1W+VRfHx8KFq0KJGRkbRq1QpXV9cMtwn3l2ctXLgw33//PW3btrXYB8ZoNGofGBEReSxKwIiIiIg8I27evMnUqVPp3Lkz8fHxxMfHA/eXgDEajZw7d44cOXKYlwQzLQX2oEuXLuHq6mreINe0TNilS5fInz+/uV5SUhLXrl2zWJosb968KbZpWqbMVPfBNlPq/9/LnYmIiMiLIVeuXEyZMoU+ffrQunVr3njjDXx9fXFycuKff/5h7dq1JCQkWOzjMmjQIHbt2kWHDh0ICgqiVKlSXL9+naNHj7J27Vr++OOPdMVgb2/P559/Tq9evXjjjTdo27Ytnp6eXLx4kV27dmE0Gpk7d266r83e3p6RI0fy1ltv0bp1a9q2bUu+fPn466+/2Lt3LwsXLkx3myIiIkrAiIiIiDwjrl+/TkJCAjNnzmTmzJlWxxs0aEDdunWZPn06Hh4eHDx40KrOgQMHKFOmjPm16d8HDx60SMAcPHgQg8FgUbd06dLs2rXLarbMgQMHcHZ2pkSJEsD9dd4dHBw4ePAgLVq0MNdLSkriyJEjNG7cOAN3QURERJ5lfn5+/Pzzz0RERLBp0yaio6O5d+8e+fLlo1KlSnz44YfUqFHDXP/ll19m0aJFTJkyhfXr17Nw4UJy5cpFiRIlGDp06GPF8Nprr/Hjjz8yZcoU5s+fT3x8PHnz5sXX15d27do99rW9/vrrzJ07l2+//ZaIiAgMBgOFCxe2GO+IiIikhxIwIiIiIs+Il19+mcmTJ1uVf//99+zdu5evv/6aPHnyANC4cWOWLFnC+fPnKViwIADbt2/n9OnTdOnSxXxu9erVcXd3Z8GCBTRo0MBcvmDBApydnalXr565rGnTpqxevZro6GiaN28O3F/GIzo6mrp165qXHnNzc6NGjRqsWrWKsLAw89Ify5cvJyEhgaZNm2bynREREZFnycsvv8ygQYMYNGhQmup7eHgwfPhwhg8fnmqdatWqcezYMavyQoUKpVheunRpJk2a9NB+w8LCUl3WNTAwkMDAQKvyihUrMmvWrIe2KyIiklZKwIiIiIg8I3LkyEHDhg2tytetW8e+ffssjoWGhhIdHU23bt3o2rUriYmJzJo1i1dffZWgoCBzvezZs/POO+/w6aefEhYWRu3atdm9ezcrVqwgLCzMYo30Jk2aUKFCBT766CNiYmLInTs3CxYsIDk5mXfffdcipgEDBtCxY0dCQkLo0KEDsbGxzJ49m+rVq1O3bt3MvzkiIiIiIiIizxklYERERESeQwUKFGDevHl88cUXTJw4EQcHB2rXrs3QoUNxdna2qBscHIyTkxOzZ89m48aNeHp6MnToULp3725RL1u2bMyYMYMvv/ySuXPnkpiYiK+vL6NHj7baZLdcuXJERETw1VdfMWbMGFxcXAgMDGTQoEHapFZEREREREQEJWBEREREnnlffPEFX3zxhVV5qVKl0rxERlBQkMXMmNTkypWLUaNGMWrUqEfWrVKlCgsWLEhT/yIiIiIiIiJZjf2jqzx9t27dYtKkSfTu3Zvq1avj7e3NjBkzUqx78uRJevXqRcWKFalatSoDBw7k8uXLKdaNioqiefPm+Pr60qhRIyIjIzEajVb1bty4wYgRI6hevToVKlSgS5cu/PHHHym2uXfvXjp37kz58uXx9/dn5MiR3Lp1y6qewWDgu+++o0GDBvj6+hIQEMDy5cvTcVdEREREREREREREROR58UzOgLl69SqTJ0/G09OTsmXLsnXr1hTrXbhwgeDgYFxdXRkwYAC3b99m1qxZHD9+nKioKIvlNxYuXMgnn3xC48aNefPNN9m9ezdjxozh9u3b9O3b11zPYDDQp08fjh07Ro8ePfDw8GDBggV07dqVqKgoi+U3jhw5Qvfu3SlRogRDhgwhNjaWiIgITp8+TUREhEWsEydOZMaMGQQFBeHn58f69esZPHgwdnZ2tGzZMpPvoIiIiIiIiIiIPAvs7O2Y9NM3tg5DRJ6iEt4lbB2CPCOeyQRMvnz5+PXXX8mfPz/nzp2jQYMGKdabNm0at27dYvHixRQsWBAAX19f3nzzTaKioggODgYgMTGRiRMnUqtWLcLDw4H7y3Dcu3ePadOm0aFDB/MGtNHR0ezbt4+JEyfSvHlzAJo1a0aTJk2YNGkS33zzvw/MCRMm4Obmxty5c3FzcwOgUKFCDB8+nM2bN1OnTh0Ac2KmY8eOjBw50tx/SEgI48aNo3nz5jg4PJM/ChERERERERERySDfKr62DsEmEhISAHBxcbFxJE9fVr520PUnJCRw586dLHv98j/P5BJkTk5O5M+f/5H11qxZQ506dczJFwB/f3+KFStGdHS0uWznzp1cu3aNTp06WZwfHBxMYmIimzZtMpetXr2a3Llz07RpU3OZh4cHzZo1Y+PGjSQmJgIQHx/Ptm3bCAgIMCdfAFq1aoWLiwu//PKLuWzdunXcvXvXon87Ozs6derEpUuX2LNnTxruioiIiIiIiIiIPG9SWv4+qzhz5gxnzpyxdRg2kZWvHXT9Z86cITY21tZhyDPgmUzApEVsbCxXrlzBx8fH6pifnx+HDx82vzb9+991y5Urh729vUXdI0eOULZsWeztLW+Nr68vd+7cISYmBoBjx46RnJxs1aaTkxNlypSxatPJyQlvb2+rOE3HRURERERERERERETkxfHcrnt18eJFAPLmzWt1LG/evMTHx5OQkICLiwuXLl0C7i9t9iAnJyfc3d3NbQFcunSJihUrWrVpOvfixYuULVvW3GZq/ZsSNaY28+TJg52dnVU9IMPZUIPBwNGjRzPUhkhWUqRIEVuHIC+ohISETHnCx2AwWD0IICIiIiIiIiIiz5fn9tudO3fuAPeTKP/m7OwMYF4uLDExEUdHR6sEiKmuqS1T3ZTaNJU92ObD+k9Lm6Y4H6wrIiIiIiIiIiIiIiLPv+d2BowpeZGUlGR1zJTQyJ49u/nvu3fvpvhE8Z07d8xtmeqm1Kap7ME2H9Z/Wto0xflg3cdhb29P6dKlM9SGSFaTaOsA5IXk4uKSKb+P9+3blwnRiIiIiIjIsyClB4Kziqy8AkVWvnbQ9YuYPLcJGNOSYKalwB506dIlXF1dcXFxAf631NelS5fInz+/uV5SUhLXrl2zWJosb968KbZpWqbMVPfBNlPq/99tbtu2zSoBZDr3wZhEREREREREROTFcnyP9v8VyQyFvYuSw9XF1mGIpNlzm4DJnz8/Hh4eHDx40OrYgQMHKFOmjPm16d8HDx60SHYcPHgQg8FgUbd06dLs2rXLKlly4MABnJ2dKVGiBABeXl44ODhw8OBBWrRoYa6XlJTEkSNHaNy4sUX/ixYt4vjx4xZPRu/fv9/cp4iIiIiIiIiIvHiMRiNjQj6ydRgiL4Rh8z7Hq3KZR1cUeUY8t3vAADRu3JjNmzdz/vx5c9n27ds5ffo0TZs2NZdVr14dd3d3FixYYHH+ggULcHZ2pl69euaypk2bcvXqVaKjo81lcXFxREdHU7duXfPSY25ubtSoUYNVq1YRHx9vrrt8+XISEhIs+m/QoAGOjo4W/RuNRhYuXEjevHmpXLlyJtwNERERERERERERERF5VjyzM2DmzZvHjRs3uHnzJgA7d+4kOTkZgC5duuDm5kZoaCjR0dF069aNrl27kpiYyKxZs3j11VcJCgoyt5U9e3beeecdPv30U8LCwqhduza7d+9mxYoVhIWF4eHhYa7bpEkTKlSowEcffURMTAy5c+dmwYIFJCcn8+6771rEOGDAADp27EhISAgdOnQgNjaW2bNnU716derWrWuu5+npSdeuXZk1axYGgwE/Pz/Wr1/P7t27GTt2LI6Ojk/wToqIiIiIiIiIiIiIyNP2zCZgZs+ebTGzZcuWLWzZsgWAli1b4ubmRoECBZg3bx5ffPEFEydOxMHBgdq1azN06FCrje2Dg4NxcnJi9uzZbNy4EU9PT4YOHUr37t0t6mXLlo0ZM2bw5ZdfMnfuXBITE/H19WX06NGULFnSom65cuWIiIjgq6++YsyYMbi4uBAYGMigQYOsNlgbNGgQ7u7uLFy4kKVLl1K0aFHGjh1L69atM++miYiIiIiFhMS7nP3nqq3DkBdI4QK5ccmuB6hEREREROTRntkEzIYNG9JUr1SpUsyaNStNdYOCgixmxqQmV65cjBo1ilGjRj2ybpUqVayWNkuJvb09ffr0oU+fPmmKVUREREQy7uw/Vxk5da2tw5AXyCd9G+FdPJ+twxARERERkefAc70HjIiIiIiIiIiIvLiWLFmCt7c3586dS/e54eHheHt7P4GoRERE0uaZnQEjIiIiIiIiIiLPhiVLljBs2LBUj3/33XfUrl37KUb0eH744Qdy5MhBYGCgrUMREZEsQAkYERERERERERFJk7CwMAoXLmxVXrp0aRtE83B9+/a1Wgp+wYIF5M6dWwkYERF5KpSAERERERERERGRNKlZsyYVKlSwdRhp4uDggIODvvoSERHb0R4wIiIiIiIiIiKSYab9WhYuXGhRPm/ePLy9vfn5558BOHfuHN7e3syYMYO5c+dSv359/Pz86NixIwcOHEhTX6tXryYwMBA/Pz+qVavG+++/z99//21R5997wNSvX58TJ06wa9cuvL298fb2pn79+ubjP/zwAy1atKBChQpUrlyZVq1aWV2LiIhIeugxABERERERERERSZObN28SFxdnVe7h4UFgYCBr165l7NixvP766xQuXJjTp08zfvx4mjVrxhtvvGFxzsqVK7lx4wadO3fGYDDwww8/0L17d5YuXUrRokVTjWH58uUMHjyYcuXK8f7773P16lW+//579uzZw9KlS/Hw8EjxvA8//JDPPvsMFxcXQkNDAciZMycAixYt4tNPP6VJkyYEBweTnJzMiRMn2LdvHx07dnzc2yUiIlmcEjAiIiIiIiIiIpImvXr1SrH8wIEDODs789lnnxEQEMCHH37I7NmzGTp0KK6urnzyySdW55w+fZpffvmFQoUKAdC0aVMCAgIIDw9n/PjxKfZz9+5dxo0bR8mSJZk/fz7Zs2cHwN/fn65du/Ldd98xZMiQFM9t2LAhX3/9Nblz56ZVq1YWxzZt2kSpUqWYNGlSmu+FiIjIoygBIyIiIiIiIiIiaTJ8+HBKlixpVe7o6AhAnjx5+PTTTwkLCyM4OJj9+/czbdo0cufObXVOvXr1zMkXgOLFi1OzZk02b96cav8HDx7k8uXLhIaGmpMvANWqVaNcuXJs2rQp1QTMw7i5uXHhwgUOHDiAn59fus8XERFJiRIwIiIiIiIiIiKSJr6+vlSoUOGhdRo3bkyTJk1YvXo1rVu3pl69einWK1asWIplmzZt4saNG7z00ktWx037vBQvXtzqWMmSJVm9evWjLyIFvXv3Zvv27QQFBVG4cGH8/f1p3rw51atXf6z2REREAOxtHYCIiIiIiIiIiLw4bty4wf79+wE4efIk9+7ds3FEj1ayZEmio6P55ptvqF69Ops2baJbt26MHDnS1qGJiMhzTAkYERERERERERHJNJ999hlxcXF88MEH/PHHH0yfPj3FeqdPn06x7KWXXkpx9gvAK6+8AsCpU6esjsXExFCwYMGHxmZnZ5fqsRw5ctC0aVNGjRrFhg0baNGiBfPnzyc2NvahbYqIiKRGCRgREREREREREckUa9euZcWKFbz77rv06tWLdu3aMWXKFI4ePWpVd+PGjZw7d878+tSpU2zZsoXatWun2r6Pjw958uThxx9/5M6dO+by3bt3c/DgQerWrfvQ+HLkyMH169etyq9evWrx2sHBAS8vL+D+jB4REZHHoT1gREREREREREQkTbZs2cJff/1lVe7n50euXLn45JNPqFixIj169ABg2LBhbN++nSFDhrBo0SKcnJzM5xQrVozg4GCCg4MxGAzMmzcPJycn+vfvn2r/jo6OfPDBBwwZMoTOnTvTsmVL4uLimDt3Lvnz56d3794Pjd/Hx4f58+fz7bffUqxYMVxcXKhfvz49e/bEw8ODypUrkydPHs6cOcO8efPw9vamZMmSj3m3REQkq1MCRkRERERERERE0iQ8PDzF8o8//phdu3aRkJDAF198gb39/UVXXF1dGT16NN27d2fy5MkMGDDAfE6LFi3IkSMHERERXLp0ibJly/Lhhx9SvHjxh8bQunVrcuTIwfTp0xk/fjw5cuSgTp06DBo0CA8Pj4ee269fP/755x8iIiKIj4+nYMGC1K9fnw4dOrBq1SrmzJlDfHw8+fLlo23btvTt29d8LSIiIumlBIyIiIiIiIiIiDxUYGAggYGBD60TEhKSYnn16tVTXIIMoEuXLnTp0iXd/TZp0oQmTZo8NJ6wsDDCwsIsyl5++WWmTp1qVbdDhw506NDhoe2JiIikl1L4IiIiIiIiIiIiIiIimUwJGBERERERERERERERkUymBIyIiIiIiIiIiIiIiEgm0x4wIiIiIiIiIiLy1BQqVIhjx47ZOgwREZEnTgkYERERERERERGRJ8TOzo5h8z63dRgiL4TC3kVtHYJIuigBIyIiIiIiIiIi8gR5VS5j6xBsIiEhAQAXFxcbR/L0ZeVrB12/iIn2gBEREREREREREXlCjEajrUOwmTNnznDmzBlbh2ETWfnaQdcvYqIEjIiIiIiIiIiIiIiISCZTAkZERERERERERERERCSTKQEjIiIiIiIiIiIiIiKSyRxsHYCIiIiIiIiIiMiLys7OztYh2EyRIkVsHYKIiE0pASMiIiIiIiIiIvKE2AFn9x6zdRg2dcXWAWSSfF5FcHbNYeswROQ5ogSMiIiIiIiIiIjIE2I0GpnT7VNbhyGZoNv3IyhcydvWYYjIc0R7wIiIiIiIiIiIiIiIiGQyJWBEREREREREREREREQymRIwIiIiIiIiIiIiIiIimUwJGBERERERERERERERkUymBIyIiIiIiIiIiIiIiEgmc7B1ACIiIiIiIiIi8vxYsmQJw4YNS/FYcHAwI0aMeMoRiYiIPJuUgBERERERERERkXQLCwujcOHCFmXFixe3UTQiIiLPHiVgREREREREREQk3WrWrEmFChVsHUaGJCQk4OLiYuswRETkBaUEjIiIiIiIiIiIZBpvb2/69+9PWFiYRXn9+vWpWrUqX3zxhfn1+fPnU2zj+++/p1q1anh7e6faz/r16ylUqBAAp06d4uuvv2bHjh0kJCRQsmRJQkNDadq0qbm+aem0yMhI1q9fz3//+1+uXLnCsWPHSE5OZsaMGSxfvpx//vmH7NmzU7RoUXr37k3jxo0zektERCSLeu4TMKdPn2bSpEns2bOHa9eu4enpSePGjenduzcvvfSSud7JkycZM2YMe/bswdHRkVq1ajFs2DDy5Mlj1WZUVBSzZ8/m7NmzeHp6EhwcTLdu3bCzs7Ood+PGDcaPH8+aNWtITEzE19eXwYMH4+vra9Xm3r17GT9+PIcOHSJnzpw0adKEQYMGkTNnzsy/KSIiIiIiIiIiT9jNmzeJi4uzKPPw8Ejz+R9++CG3bt2yKIuMjOTo0aO4u7sDMG7cOKvzvv76a+Li4swzV06ePEnHjh3JkycPPXv2JGfOnKxdu5Z3332XcePG0apVK4vzR40axUsvvURoaCg3b94E4Ntvv2XatGm0a9cOPz8/EhISOHLkCAcOHFACRkREHttznYD5559/CAoKImfOnHTq1AkPDw8OHjzIrFmz2LVrFz/++CMAFy5cIDg4GFdXVwYMGMDt27eZNWsWx48fJyoqCmdnZ3ObCxcu5JNPPqFx48a8+eab7N69mzFjxnD79m369u1rrmcwGOjTpw/Hjh2jR48eeHh4sGDBArp27UpUVBQlS5Y01z1y5Ajdu3enRIkSDBkyhNjYWCIiIjh9+jQRERFP74aJiIiIiIiIiGSSXr16WZXt3bs3zec3bNjQ4vXKlSs5fPgw77zzjnnmy7+TJ9OmTePvv/9m7Nix5mTP559/Tr58+Vi8eDHZs2cHIDg4mB49evDVV1/RsmVLi4dqXVxcmDt3Lg4O//tabNOmTdSpU4dRo0alOX4REZFHea4TMMuXL+fGjRvMmzfP/MHcvn17cuTIQWRkJCdPnqRkyZJMmzaNW7dusXjxYgoWLAiAr68vb775JlFRUQQHBwOQmJjIxIkTqVWrFuHh4QAEBQVx7949pk2bRocOHcwf7tHR0ezbt4+JEyfSvHlzAJo1a0aTJk2YNGkS33zzjTnOCRMm4Obmxty5c3FzcwOgUKFCDB8+nM2bN1OnTp2nc8NERERERERERDLJ8OHDLR5ABcwJkPQ6fPgww4cPp0GDBrz99tsp1tm8eTPffPMNXbp0oXXr1gBcu3aNbdu20b9/fxISEkhISDDXr1WrFlu3buXUqVOUKFHCXN6+fXuL5AuAm5sbJ06c4NSpUxQvXvyxrkFEROTf7G0dQEaYponmzZvXotz02vShv2bNGurUqWNOvgD4+/tTrFgxoqOjzWU7d+7k2rVrdOrUyaK94OBgEhMT2bRpk7ls9erV5M6d22ItUQ8PD5o1a8bGjRtJTEwEID4+nm3bthEQEGBOvsD9JzhcXFz45ZdfMnILRERE5AVy6NAh+vfvT/369fHz86N69eqEhISwceNGq7onT56kV69eVKxYkapVqzJw4EAuX76cYrtRUVE0b94cX19fGjVqRGRkJEaj0arejRs3GDFiBNWrV6dChQp06dKFP/74I8U29+7dS+fOnSlfvjz+/v6MHDnSagkRERERebH5+vri7+9v8SdbtmzpbicuLo7+/ftToEABxo0bZ7UEPNxfgn7QoEFUrlyZoUOHmsvPnDmD0WgkPDycGjVqWPwx7TXz72XSChcubNX+O++8Q3x8PE2bNuWNN95gzJgxqY6DRERE0uq5ngFTtWpVZs6cyYcffkhYWBgvv/wyf/zxB7NmzaJFixYULFiQ2NhYrly5go+Pj9X5fn5+bNiwwfz68OHDAFZ1y5Urh729PYcPHyYwMBC4v6xY2bJlsbe3zGH5+vry448/EhMTQ9myZc0buf27TScnJ8qUKWPuU0REROTs2bMkJSURGBhI/vz5SUhIYM2aNYSGhvKf//zH/JCIllcVERGR59G9e/esypKTk3nvvfe4du0aixYtwtXV1arOrVu36NevHy4uLnz99dcWs1cMBgMA3bt3T3WFkVKlSlm8TmmWzmuvvcbatWvZuHEjW7ZsYdmyZcyZM4eBAwfSu3fvdF2niIiIyXOdgKlTpw5hYWF89913Fk+Gdu7cmY8//hiAixcvAtazZExl8fHxJCQk4OLiwqVLlwDIly+fRT0nJyfc3d3NbQFcunSJihUrWrVpOvfixYuULVvW3GZq/cfExKTrmlNiMBg4evRohtsRySqKFCli6xDkBZWQkMCZM2cy3I7BYLBK8EvW0LRpU4vZtQAhISEEBgYSERFhTsBoeVURERFLf//9Nx4eHqkuf5WYmEhcXByvvPLKU44sa8qVKxc3btywKEtKSjJ/R/KgcePGsWvXLr799lur5cwAjEYjQ4YM4cyZM/zwww/kyZPH4rhpNku2bNnw9/fPcNytW7emdevWJCYm0rt3b8LDw+nRo8djzewRERF57r/dKVy4MBUrVuSTTz5h8uTJdO3alR9//JHx48cDcOfOHeB+EuXfTE+HmpYLS0xMxNHRMcWprs7Ozua2THVTatNU9mCbD+v/wTZFRERE/i1btmx4enpafImh5VVFREQsNWjQgLVr16Z6fMOGDTRo0OApRpS1FS5cmN27d1uU/fTTT1YzYJYvX86cOXMIDQ2lYcOGKbY1depU1q5dy4gRI/Dz87M6/vLLL1OtWjUWLVpEbGys1fF/Lz+WmqtXr1q8zp49OyVKlODOnTvmcZCIiEh6PdczYH7++WeGDx/Of//7X/MTDw0bNsTV1ZWpU6fSqlUrc5IlKSnJ6nxT8sP0hEz27Nm5e/duik8e37lzx2I5j+zZs6fYpqnswTYf1v+DbT4ue3t7SpcuneF2RLISDZ/lSXBxccmU38f79u3LhGjkeXbr1i3u3LnDzZs3Wb9+Pb/99hvNmjUDyBLLq2bW7F7NeJQnJbNmPGYWvdflScmM9/rTmtmb0t5m/44jpYct5ckICgrik08+ISwsDH9/f44ePcqWLVvInTu3uc7Vq1cZMWIEefPmpVixYixfvtyijddff524uDjCw8N59dVXcXJysqrTqFEjXFxczEu1tmzZkqCgIIoUKcKVK1fYv38/J0+efGhyzqR58+a89tpr+Pj4kDt3bo4dO0ZUVBT16tUjZ86cmXNjREQky3muEzDz58+ndOnSVpunNWzYkClTprB3717q168PkOI010uXLuHq6oqLiwvwv2XCLl26RP78+c31kpKSuHbtmsXSZHnz5k2xTdMyZaa6D7aZUv//Xu5MRERE5JNPPmHlypXA/QctGjVqxIgRI4CssbyqiIjI43hYguXkyZMWMzflyWrfvj3nzp0jKiqK3377jcqVKxMREUH37t3NdW7dukViYiKJiYkMGTLEqo3vv/8euJ88+/PPPxk8eLBVnfXr1+Pi4kKJEiVYvHgxkydPZtmyZVy7do3cuXNTunRp3n333TTF3K1bNzZs2MCOHTtITEykQIEC9O7dW/u/iIhIhjzXCZjLly+n+BSCaUrrvXv3yJ8/Px4eHhw8eNCq3oEDByhTpoz5tenfBw8etEjAHDx4EIPBYFG3dOnS7Nq1y+ppngMHDuDs7EyJEiUA8PLywsHBgYMHD9KiRQtzvaSkJI4cOULjxo0f9/JFRETkBfXWW28RGBjIxYsXWbVqFffu3TPPpk3r8qouLi7P7fKqmTu7Nz6T2hH5n8ya8ZiZNLtXnoTMeK8/yZm9S5cuZenSpebXU6dO5aeffrKqd/36dU6cOJHqEleSfoGBgeZZtCmxt7dn0KBBDBo0yKL8wZm6hQoV4tixY4/sKy11TO2NGTPmoXUeFndoaCihoaFp6ktERCStnus9YIoXL86xY8c4ceKERfmKFSuA+8trADRu3JjNmzdz/vx5c53t27dz+vRpi3XOq1evjru7OwsWLLBob8GCBTg7O1OvXj1zWdOmTbl69arFOutxcXFER0dTt25d89Jjbm5u1KhRg1WrVhEf/78vAJYvX05CQoLVRrsiIiIipUqVwt/fn9atW/Pdd9+RkJBA3759MRqNj728akp1n9XlVUVERNLixo0bnDt3jnPnzmFnZ0dcXJz5tenP+fPnMRgMtG3blv/85z+2DllERESymOd6BkyvXr347bffCAkJISQkhDx58rBjxw6io6OpWbOmeRmN0NBQoqOj6datG127diUxMZFZs2bx6quvEhQUZG4ve/bsvPPOO3z66aeEhYVRu3Ztdu/ezYoVKwgLC8PDw8Nct0mTJlSoUIGPPvqImJgYcufOzYIFC0hOTraa3jpgwAA6duxISEgIHTp0IDY2ltmzZ1O9enXq1q37VO6ViIiIPJ/s7Oxo2rQpI0aM4NSpU+YlwbS8qoiIZHXdunWjW7duwP1VKj788EOLlSdEREREbO25TsBUqVKFH3/8kW+//ZZFixYRFxdHvnz56N27N/379zfXK1CgAPPmzeOLL75g4sSJODg4ULt2bYYOHWr1lGZwcDBOTk7Mnj2bjRs34unpydChQy3WKQXIli0bM2bM4Msvv2Tu3LkkJibi6+vL6NGjKVmypEXdcuXKERERwVdffcWYMWNwcXEhMDCQQYMGaRNAEREReSTTsl/x8fGUKFFCy6uKiIj8y/r16y0emhQRERF5FjzXCRgAHx8fpk2b9sh6pUqVYtasWWlqMygoyGJmTGpy5crFqFGjGDVq1CPrVqlSxWppMxEREZEHXblyhZdfftmiLCkpiWXLlpE9e3bzQx6NGzdmyZIlnD9/noIFCwL/W161S5cu5nMfXF61QYMG5vLUllddvXo10dHRNG/eHHj08qphYWG4uroCWl5VRERsy/R5KCIiIvIsee4TMCIiIiIvigEDBuDk5ETFihXJly8fsbGxrFy5ktOnTzN06FBy5swJaHlVERGRlNy4cYOoqCj279/P9evXrfZAs7OzY86cOTaKTrIyOzs7un0/wtZhSCbI51XE1iGIyHNGCRgRERGRZ0SrVq1YtmwZP/zwA9evX8fV1ZVy5coxePBgixksWl5VRETE0j///EOnTp24cOECbm5uxMfHkytXLm7cuIHBYCB37tzkyJHD1mFKFmUEClfytnUYNpGQkABg3qNQRCSrUQJGRERE5BnRtm1b2rZtm6a6Wl5VRETkf7755huuXbtGREQE3t7e+Pv7M3HiRCpUqMDkyZOJjo7mhx9+sHWYkkUZjUZbh2AzZ86cAe7vNygikhXZP7qKiIiIiIiIiMiza9u2bbRr144aNWpYzMbMkSMHgwYNomTJknz11Vc2jFBERESyIiVgREREREREROS5FhcXh7f3/SWeHBzuL/Zx584d8/GaNWuydetWm8QmIiIiWZcSMCIiIiIiIiLyXHN3d+fmzZsAuLq64ujoyN9//20+bmdnx61bt2wVnoiIiGRRTywB8/vvv7N27Vri4+OfVBciIiIiIiIiIhQvXpw///wTuJ9sKVeuHEuXLiUpKYnbt2+zZMkSihQpYuMoJat6cFm8rKZIkSL6b09EsjSHjDYwffp0du7cyezZs81l/fv3Z/369QB4enqycOFC8ufPn9GuRERERERERESsvP7668ycOZNPPvkEZ2dnevXqRf/+/alatSp2dnYkJiby+eef2zpMyaLsgIt7j9k6DJtK7+PZub2K4Oia44nEIiLyNGU4AbN69WrKly9vfr1t2zbWrVtHixYt8Pb2ZvLkycyaNYsPP/wwo12JiIiIiIiIiFh566236NGjB05OTgA0bNiQb7/9luXLl2Nvb0/Tpk1p1qyZjaOUrMpoNLK262e2DuO50mjux+Sr5G3rMEREMizDCZi///6bNm3amF+vW7eOPHnyMHbsWOzt7bl8+TIbN25UAkZEREREREREngg7Oztz8sWkYcOGNGzY0EYRiYiIiGTCHjAJCQm4uLiYX+/Zs4caNWpgb3+/6VdffZWLFy9mtBsREREREREREREREZHnRoYTMHnz5uXUqVMAXL58mePHj1OlShXz8Zs3b+LgkOGJNiIiIiIiIiIiKQoPDycgICDFY0ajkRYtWjBlypSnHJWIiIhkdRnOjPj7+zN//nzc3d3ZuXMn9vb21K5d23z81KlTeHp6ZrQbEREREREREZEUrV27lho1aqR4zM7ODn9/f9asWcPbb7/9lCMTERGRrCzDM2D69+9P/vz5GT9+PL/99ht9+/alQIECACQnJ7N27VqLGTEiIiIiIiIiIpnp3LlzlCxZMtXjxYsX59y5c08xIhEREZFMmAGTP39+Vq1axZ9//ombmxuvvPKK+VhiYiKfffYZpUuXzmg3IiIiIiIiIiIpMhqNxMfHp3o8Pj6e5OTkpxiRPAlLlixh2LBh5tdOTk7kypULb29v6tSpQ2BgIK6uro/V9p9//snkyZPZv38/ly5dIleuXBQrVoxq1aoRFhaWWZcgIiJZTKZszpItWza8vb2tyl1dXWnYsGFmdCEiIiIiIiIikqLixYvz66+/0qtXrxSP//rrrxQtWvQpRyVPSlhYGIULFyY5OZnLly+za9cuRo8eTWRkJFOmTEn3g8D79u2ja9eu5MuXj8DAQPLnz09sbCyHDh1ixowZSsCIiMhjS3cC5u+//36sjh6cGSMiIiIiIiIikllatmzJF198wZgxYxgwYADZs2cH7q/M8c033/D7778zaNAgG0cpmaVmzZpUqFDB/Pqtt95i+/bthIaG8vbbb/Pf//7X/B5Ii6lTp+Li4kJUVBS5c+e2OHb58uXMCltERLKgdCdg6tevj52dXbo7OnLkSLrPERERERERERF5lJCQEDZt2sScOXNYtGgRxYoVA+D06dMkJCRQtWpVunXrZtsg5YmqUaMGb7/9NhMmTGDFihW0b98egF27djFp0iQOHjxItmzZqFKlCgMHDsTLy8t87pkzZyhZsqRV8gUgT548T+0aRETkxZPuBEy/fv2sEjAbNmzgyJEj1KhRg1dffRWAEydOsGPHDsqWLUu9evUyJ1oRERERERERkX9xcHBg5syZzJkzh5UrVxITEwNAsWLFaNmyJV27dsXBIVNWYZdnWKtWrZgwYQJbtmyhffv27Nixg549e1KoUCH69+/PnTt3mD9/Pp06dSIqKorixYsDULBgQfbu3cvRo0e1j7GIiGSqdI8+/r3uZXR0NBERESxcuJDy5ctbHNu3bx89e/akT58+GYtSREREREREROQhHBwc6NmzJz179nxkXYPBwIULF8iTJw9OTk5PITp5Gjw9PXFzc+Ps2bMAjB07FldXVxYuXGie3fLGG28QEBDAxIkTmTRpEgC9evWiR48etGnTBh8fH6pUqUK1atWoUaMGzs7ONrseERF5/tlntIEZM2bQuXNnq+QLQMWKFencuTPTp0/PaDciIiIiIiIiIpkiLi6OBg0asGfPHluHIpnMxcWFW7ducfHiRQ4fPkybNm0slhYrVqwY9evX57fffuPevXvA/eXLfvjhB+rWrcuJEyeYPXs2b731Fv7+/ixevNhWlyIiIi+ADCdgTp48SYECBVI9/sorr3Dy5MmMdiMiIiIiIiIikmmMRqOtQ5AnICEhgZw5c/L3338DmJcZe1CJEiVISEjg6tWr5rJKlSoxdepUfv/9d5YvX857772HnZ0dH374Idu3b39q8YuIyIslwwkYNzc3duzYkerxHTt24ObmltFuREREREREREREUnXhwgVu3rxJkSJFHrsNR0dHSpcuTd++fQkPDwdgxYoVmRWiiIhkMRlOwDRt2pS1a9fyxRdfEBcXZy6Pi4tjzJgxrF27lqZNm2a0GxERERERERERkVQtX74cgJo1a/LKK68AcOrUKat6MTExuLi4WCxNlhI/Pz8ALl68mMmRiohIVuGQ0QYGDBjAoUOHiIyMZM6cOeYPr6tXr2I0GqlQoQIDBgzIcKAiIiIiIiIiIiIp2b59O1OmTKFQoUK0bNkSZ2dnypUrx7JlywgNDcXd3R2AM2fOsGHDBurXr0+2bNnM51arVg17e8vnlDdv3gzcX7JMRETkcWQ4AZMzZ07mzZvH0qVLWbNmDWfPngXA19eXRo0a0aZNG/MHmoiIiIiIiIiISEZs2bKFv/76i3v37nH58mV27tzJ1q1beeWVV5g6dSrOzs4ADB48mJ49e9KhQwfat2/PnTt3mD9/Ps7OzhYPC3/++eckJCTQsGFDSpYsicFg4PDhwyxfvhx3d3e6detmq0sVEZHnXIYSMImJiURHR1O8eHHatWtHu3btMisuERERERERERERK6a9WRwdHXF3d8fLy4sPP/yQwMBAXF1dzfWqV6/O7NmzmTRpEpMmTSJbtmxUqVKFgQMHUrx4cXO9wYMHs2bNGrZs2UJUVBRJSUnky5ePFi1aEBoaSqFChZ76NYqIyIshQwkYJycnhg8fzkcffUT58uUzKyYRERERERERERELgYGBBAYGpuucatWq8cMPPzy0Tu3ataldu3ZGQhMREUmR/aOrPORke3vy5ctHQkJCZsUjIiIiIiIiIiIiIiLy3MtQAgagadOmREdHc+/evcyIR0REREREREQkXW7cuPHIOkePHjX/283NjTFjxlCqVKknGZaIiIhkcRlOwLRt25bk5GS6d+/Ohg0bOHnyJH///bfVHxERERERERGRJ+Htt9/m7t27qR7/888/6dGjh/m1s7Mzbdq0IU+ePE8jPBEREcmiMrQHDMAbb7yBnZ0dRqOR3bt3p1rvyJEjGe1KRERERERERMTKwYMHGTJkCBMmTLA69tdff9G9e3ecnZ1tEJmIiIhkZRlOwPTr1w87O7vMiEVEREREREREJN3Gjx/PO++8g6enJ4MHDzaX//3333Tv3h2AyMhI2wQnWZ6dnR2N5n5s6zCeK7m9itg6BBGRTJHhBExYWFhmxCEiIiIiIiIi8lgaNmzIsGHD+PzzzylUqBCdO3fm4sWLdO/enYSEBObNm0fRokVtHaZkUUYgXyVvW4dhEwkJCQC4uLjYOBIREdvIcAJGRERERERERMTWunTpwt9//83nn3+Ok5MTkZGRxMXFERkZSalSpWwdnmRhRqPR1iHYzJkzZwAoXbq0jSMREbGNTEvArFy5kjVr1ph/sRYpUoQmTZoQEBCQWV2IiIiIiIiIiKRqyJAh/P3333z88cfkyJGDWbNm4ePjY+uwREREJIvKcALm7t279OvXj99++w2j0Yirqyt2dnYcO3aMdevWsWLFCqZMmYKDgybbiIiIiIiIiEjGLVu2LNVjNWrUYOvWrTRq1Ii//vqLv/76y3ysdevWTz44ERERkf8vw1mR7777jl9//ZXAwEDCwsIoUKAAABcuXODbb78lKiqKmTNnEhoamuFgU3P48GHCw8PZu3cviYmJFCxYkDZt2tC7d29znb179zJ+/HgOHTpEzpw5adKkCYMGDSJnzpwWbRkMBmbNmsXChQu5ePEiRYsWpXfv3rRq1cqq39jYWMaMGcPWrVtJTk6mWrVqDBs2LMV1ZdevX8/kyZP5888/yZ07N23atKFfv344Ojpm/g0REREREREReYENHToUOzu7hy7ttHTpUpYuXWp+bWdnpwSMiIiIPFUZTsCsXLmSOnXqMHr0aItyT09PRo0axcWLF1m+fPkTS8Bs2bKF0NBQypYtS9++fXFxceHs2bNcuHDBXOfIkSN0796dEiVKMGTIEGJjY4mIiOD06dNERERYtDdx4kRmzJhBUFAQfn5+rF+/nsGDB2NnZ0fLli3N9W7dukXXrl25efMmffr0wdHRkcjISIKDg1mxYgUeHh7mups3b6Zfv3689tprDB8+nOPHjzN9+nQuX77MqFGjnsh9EREREREREXlRff/997YOQSTN7OzsbB2CzRQpUsTWIYiI2FSGEzDnz58nJCQk1eN16tRh7NixGe0mRfHx8QwZMoS6desyadIk7O3tU6w3YcIE3NzcmDt3Lm5ubgAUKlSI4cOHs3nzZurUqQNgTsx07NiRkSNHAhAUFERISAjjxo2jefPm5qXU5s+fz+nTp/nxxx+pUKECALVq1aJFixbMnDmTwYMHm/sfN24cpUqVIiIiwnx+zpw5mT59Ot26ddNmgCIiIiIiIiLpULVqVVuHIJJmdsD1A0dtHYZNXQdyliyCQ04XW4ciIvJUZTgBkyNHDq5cuZLq8StXrpAjR46MdpOilStXcvnyZQYMGIC9vT23bt0iR44cFomY+Ph4tm3bRkhIiDn5AtCqVStGjx7NL7/8Yk7ArFu3jrt379KpUydzPTs7Ozp16sTAgQPZs2cP1apVA2D16tWULVvWnHwBKFmyJDVq1CA6OtqcgPnzzz/5888/+eijjyz2wencuTPTpk0jOjpaCRgRERERERERkReU0Wjkj7BPbB2GzfmGjySXX2lbhyEi8lSlPGUkHSpUqMCCBQs4e/as1bG///6bhQsXUrFixYx2k6Lt27fj6upKbGwsTZo0oVKlSlSqVInhw4dz+/ZtAI4dO0ZycjI+Pj4W5zo5OVGmTBkOHz5sLjty5AhOTk54e3tb1PXz8zMfh/v7xBw7dsyqTQBfX1/Onz/P9evXAczt/7tu/vz58fT0tOhfRERERERERNIvPDycgICAFI8ZjUZatGjBlClTnnJUIiIiktVleAbM22+/TXBwMC1btqRVq1bm2Rx//vkny5cv5+7du7z99tsZDjQlp0+f5t69e7z99tu0a9fOPEtlzpw5xMXFMWXKFC5dugRA3rx5rc7PmzcvMTEx5teXLl0iT548Vmtzms6NjY0F4Nq1ayQlJaXaJsDFixfJlSvXI/u/ePHi41y6BYPBwNGjWXsqq0h6aA1aeVISEhI4c+ZMhtsxGAypLqspIiIiItbWrl1LjRo1UjxmZ2eHv78/a9aseWLfT4iIiIikJMMJmPLlyzNlyhT+85//sHDhQotjBQsW5D//+Y95BklmS0hI4Pbt23Ts2JHhw4cD0LhxYwAiIyM5evQoiYmJwP0ZL//m7OzMnTt3zK8TExNTrQeY65r+flhdU7+P6t80U0ZEREREREREHs+5c+fo3LlzqseLFy/O4sWLn2JEIiIiIpmQgAGoXbs269at49ChQ+alyIoUKULZsmWf6BO82bNnB7CaZtyiRQsiIyPZs2cPL7/8MgBJSUlW59+5c8ecMDG1l1o9+F9yxfT3w+qaYjP9nZb+H5e9vT2lS2sNTZH0SLR1APJCcnFxyZTfx/v27cuEaERERESyDqPRSHx8fKrH4+PjSU5OfooRiYiIiGTCHjDmhuzt8fX1pXnz5jRv3hwfH58nvnxKvnz5AMxJFpM8efIAcOPGDfPSX6alwB506dIlcxtwf0mwy5cvYzAYrOrB/X1bANzd3XFyckq1zQdjS0//IiIiIiIiIpJ+xYsX59dff031+K+//krRokWfYkQiIiIimZCAOXz4MD/88EOKx4xGIz/88IN58/rMVq5cOeB/e7OYXLhwAQAPDw+8vLxwcHDg4MGDFnWSkpI4cuQIZcqUMZeVKVOGpKQkjh8/blF3//79AOanmu3t7fHy8rJqE+DAgQO88sor5MqVy9wmYFU3NjaWCxcuWPQvIiIiIiIiIunXsmVLdu3axZgxY8xLgcP9ZcHHjh3L77//TsuWLW0Y4YulS5cudOnSxdZhiIiIPPMynID59ttv2bhxY4rH7Ozs2Lx5M1OnTs1oNylq1qwZAFFRURblP/30E/b29tSoUQM3Nzdq1KjBqlWrLKYjL1++nISEBJo2bWoua9CgAY6OjixYsMBcZjQaWbhwIXnz5qVy5crm8iZNmnD48GFzcgYgJiaGHTt2WLRZqlQpSpQowaJFiyymO5v6aNKkSUZvg4iIiIiIiEiWFhISQvXq1ZkzZw7+/v4EBgYSGBiIv78/ERERvPbaa3Tr1i1dbS5ZsgRvb2/zn7Jly1KrVi2GDh1q9SCoiIiISEoyvAfMwYMHCQkJSfX4a6+9xrx58zLaTYrKli1L27ZtWbx4McnJyVSrVo09e/awatUqunTpQpEiRQAYMGAAHTt2JCQkhA4dOhAbG8vs2bOpXr06devWNbfn6elJ165dmTVrFgaDAT8/P9avX8/u3bsZO3Ysjo6O5rqdO3dm0aJF9O3blx49euDg4EBkZCQeHh707NnTIs7Bgweb6wUEBHDixAnmzZtHYGAg3t7eT+TeiIiIiIiIiGQVDg4OzJw5kzlz5rBy5UpiYmIAKFasGC1btqRr1644ODzeVyBhYWEULlyYpKQk/u///o+lS5eav3vIjH1dn0ezZs2ydQgiIiLPhQwnYOLi4nB3d0/1eK5cuYiLi8toN6kaOXIkr7zyCkuWLGH9+vV4enoycOBAevXqZa5Trlw5IiIi+OqrrxgzZgwuLi4EBgYyaNAg7OzsLNobNGgQ7u7uLFy4kKVLl1K0aFHGjh1L69atLeq5uroyd+5cRo8ezdSpUzEYDFStWpWhQ4ea96AxqVevHt9++y2TJ0/ms88+w93dnT59+tCvX78ndl9EREREREREshIHBwd69uxp9VBkRtWsWZMKFSoAEBQURO7cufnuu+9Yv349zZs3z9S+nhdOTk62DkFEROS5kOEETO7cuTl16lSqx2NiYnjppZcy2k2qHB0d6d+/P/37939ovSpVqlgsLZYae3t7+vTpQ58+fR5Z19PTk0mTJqUpzoYNG9KwYcM01RURERERERGRZ1OVKlX47rvvOHv2rLns1KlTfP311+zYsYOEhARKlixJaGioxRLlADdv3mTKlCmsWbOG2NhYcufOTdWqVRk8eDD58+cH7j/oOnHiRDZs2MCNGzcoWrQoXbt2pX379uZ2zp07R4MGDRg4cCDu7u7MmDGDCxcu4O3tzSeffIKfn5+57tChQ/n5559Zt24dI0eOZPv27Tg7O9OmTRsGDRpEtmzZzHVnz57N2rVriYmJISEhgWLFitG1a1eCgoIsrsO0/8vcuXPNZf/973+ZNWsWp06dwmg04unpSUBAgPnh0+TkZGbMmMHy5cv5559/yJ49O0WLFqV37940btwYgKNHjzJnzhx+//13YmNjcXFxwd/fnw8++IBXXnnF3NeSJUsYNmwYc+fOZf369SxfvpzExERef/11PvvsMzw8PCzi3bp1K9OnT+fgwYMYjUaKFy9Op06dLK7rwIEDhIeHs3fvXu7evUu5cuV49913qV69elreFiIiIinKcALmtddeY9GiRYSEhFCwYEGLY+fPnycqKorXX389o92IiIiIiIiIiDzUlStXOHjwINeuXcNoNFod//fqFo/j/PnzAOaHTU+ePEnHjh3JkycPPXv2JGfOnKxdu5Z3332XcePG0apVKwASEhIICQnhxIkTtGnTBh8fH65du8bmzZv566+/yJ8/P3fu3KFbt27ExMTQuXNnihQpwrp16/j444+5du2a1cOi//3vf0lISKBDhw7Y2dkxc+ZMwsLCWLduncUy6kajkV69euHr68vgwYPZvn07s2fPpnDhwnTu3NlcLzIykrp169KsWTPs7OxYv349w4cPJzk5mU6dOqV6T7Zt28b7779P9erVGThwIPb29pw6dYq9e/ea63z77bdMmzaNdu3a4efnR0JCAkeOHOHAgQPmBMy2bds4deoUrVu3Jl++fJw5c4aFCxdy4MABVq1aRY4cOSz6HT16NLly5aJ///6cP3+eOXPm8Omnn/L111+b6yxbtoyhQ4dSsmRJevXqhbu7O8eOHWPTpk3mBMyuXbvo2bMnZcqUoV+/fjg4OLB8+XJ69uzJ7NmzqVatWprfHyIiIg/KcALmrbfeYt26dbRp04Zu3bpRtmxZAA4fPsz3339PUlISoaGhGQ5URERERERERCQlBoOBUaNG8dNPP3Hv3r1U6z1OAubmzZvExcWRlJTE/v37+fbbb3FycqJevXoAfP755+TLl4/FixeTPXt2AIKDg+nRowdfffUVLVu2xM7OjlmzZnH06FG+/vprmjVrZm6/b9++5mTRjz/+yPHjx/niiy9o06YNcH8P2p49exIeHm5eAs3kn3/+Yc2aNeTKlQuA4sWL8/bbb7NlyxZzfAB3796lSZMm5tVDOnXqRJs2bYiKirJIwKxevdoiydGlSxd69OjB7NmzH5qA2bRpEzlz5mTWrFkWM2r+XadOnTqMGjUq1XY6depEjx49LMrq169Pp06dWLNmjTmZZeLu7k5ERIR5eXmDwcDcuXO5efMmbm5uxMfH89lnn1G2bFnmz59v/vkA5ntuNBoZMWIElStXtmirY8eOtGnThokTJ7Jw4cJUYxYREXmYDCdgvL29CQ8PZ+jQoYSHh5s/qIxGIx4eHoSHh1OmTJkMBypPR0LiXc7+c9XWYcgLpHCB3Lhkd3x0RRERERERkccUGRnJ/PnzCQgIoGbNmgwdOpSBAweSM2dO5syZQ65cuRgwYMBjtf3gHrMAhQoV4ssvv8TT05Nr166xbds2+vfvT0JCAgkJCeZ6tWrVYuvWrZw6dYoSJUqwevVqSpUqZZF8MTF9l7J582Y8PDxo2bKl+Vi2bNno1q0b27dvZ/v27Rb7zjRp0sScfIH7y6MBFsujmTy4hBlA5cqVWbFihUWZKfly9+5dbt26hcFgoFq1amzdutWc1EiJm5sbt2/fZsuWLdSpUyfVOidOnODUqVMUL148xToPJn9u3bpFUlISxYoV46WXXuLQoUNWCZh27dpZ7O1bpUoVIiMjOX/+PKVLl2bLli3Ex8fTp08fi+QL/O+eHz16lFOnTtGrVy+uXrX8PsTf35958+Zx+/Ztq9k3IiIiaZHhBAxAnTp12LhxI1u2bOH06dMAFCtWjJo1a1p9wMmz7ew/Vxk5da2tw5AXyCd9G+FdPJ+twxARERERkRfY0qVLef311xk/frz5S3QfHx9q1KhBy5YtadmyJUePHqVGjRrpbnv48OGULFmSmzdvsnTpUn7//Xfzdx1nzpzBaDQSHh5OeHh4iufHxcVRokQJzpw5Q4MGDR7a1/nz5ylatKjVLJKSJUsC9/d+eVCBAgUsXpuSMTdu3LAod3R0JF++fFZ1r1+/blG2bt06pkyZwtGjR61mEj0sAdO5c2eio6Pp06cP+fLlw9/fn8aNG1O/fn1zouOdd96hX79+NG3alFdffZWaNWsSEBCAr6+vuZ3r16/z1VdfsXr1aq5du2bV/7/9+/pNy8KZrv/MmTMAlCpVKsW4AfO+xh999FGqda5du6YEjIiIPJZMScAAZM+eXZvMi4iIiIiIiMhTd+bMGdq1aweAvb09gDmB4OrqSmBgIIsWLeLNN99Md9u+vr5UqFABgIYNGxISEsL7779PdHQ0BoMBgO7du6c68+NhX/5nVGrLff17/5sHZ4mkZvfu3fTv35/KlSszcuRI8uXLh6OjI5s3byYyMtJ8rSl5+eWXWbp0Kdu3b+fXX3/lt99+Y9myZdSrV4+pU6diZ2fHa6+9xtq1a80P8C5btow5c+YwcOBAevfuDcB7773H3r17efPNNylbtiw5c+bEzs6OAQMGpLinT1qv/2FMdQcOHIiPj0+KdTw8PNLcnoiIyIMyLQFz7tw5tm/fzuXLl2nRogWFChUiKSmJy5cvkydPHpycnDKrKxEREZEX0oEDB1i2bBk7d+7k/PnzuLu7U758ed577z2rpTpOnjzJmDFj2LNnD46OjtSqVYthw4aRJ08eq3ajoqKYPXs2Z8+exdPTk+DgYLp162b1ZcyNGzcYP348a9asITEx0bxR74NPpprs3buX8ePHc+jQIXLmzEmTJk0YNGgQOXPmzNybIiIikgaOjo44OzsD/1vG6sHlpPLnz8/58+cz3E+2bNkYOHAgwcHBzJs3j7Zt25rL/f39H3pukSJFOHHixEPrFCxYkMOHD3Pv3j2L5EJMTAxwf/mzJ2X16tU4Ozsze/Zs870E2LlzZ5rOd3Jyok6dOtSpUwej0chXX33Fd999x969e6lcuTJwf9ZN69atad26NYmJifTu3Zvw8HB69OhBfHw827ZtIywszLxXDcCdO3esZvSkVZEiRQA4ceKEeRbRvxUuXBiAnDlzPvJnKCIikl72mdHIhAkTaNKkCR9//DGTJk0yrzWalJTEG2+8wYIFCzKjGxEREZEX2syZM1mzZg01atTgo48+on379uzevZvAwECOHTtmrnfhwgWCg4M5ffo0AwYMoGfPnvz222+8+eab3Llzx6LNhQsX8tFHH1GyZElGjBhBpUqVGDNmDNOmTbOoZzAY6NOnDytXriQ4OJgPPviAq1ev0rVrV06ePGlR98iRI3Tv3p2EhASGDBlCUFAQixcvtviyRERE5GkqUKCAeXkuJycnChYsyO7du83H9+/fb7F5fUZUqVKFihUrMmfOHFxdXalWrRqLFi0iNjbWqm5cXJz5302aNOHEiRP88ssvVvVMszDq1q1LXFwcK1euNB8zGAx8//33ODk5PdYSammVLVs27OzsLGa6XL9+ncWLFz/y3H/vnWJnZ0fZsmWB/y0d9u862bNnp0SJEty5c4fExERzwunfs1ceNfvmYWrWrImrqyszZswgMTHR4pipHx8fH4oWLUpkZCTx8fFWbTz4MxQREUmvDM+AWbRoETNmzCAkJIR69erRs2dP8zFXV1fq1avHxo0b6datW0a7EhEREXmhde/enfHjx1vMHG7evDktWrRg+vTpTJgwAYBp06Zx69YtFi9eTMGCBYH7y6O8+eabREVFERwcDEBiYiITJ06kVq1a5nXpg4KCuHfvHtOmTaNDhw7mJTWio6PZt28fEydONG/u26xZM5o0acKkSZP45ptvzDFNmDABNzc35s6da14LvlChQgwfPpzNmzenugSLiIjIk1KlShU2bdrEoEGDgPufn7NmzSIpKQmDwcDKlSutNqHPiB49ehAWFkZUVBT/+c9/6NSpEy1btiQoKIgiRYpw5coV9u/fz8mTJ1m79v4+qz179mTNmjUMHDiQrVu3Uq5cOW7evMmvv/7KO++8Q9WqVWnfvj0//fQTw4cP58iRIxQuXJh169axfft2Bg4cmGlJpJTUq1ePiIgI3nzzTVq1asX169f56aefyJMnD5cuXXroucOHD+fq1avUqFEDT09PYmNj+eGHH8ibNy9VqlQB7v9MXnvtNXx8fMidOzfHjh0jKiqKevXqmWfQVq1alZkzZ3L37l1eeeUV9uzZw++//467u/tjXZOrqysfffQRH374IW3btiUgIAB3d3f+/PNPYmNj+fbbb7G3t+fzzz+nV69evPHGG7Rt2xZPT08uXrzIrl27MBqNzJ0797H6FxERyXACZv78+TRo0MD8Yftv3t7ezJ8/P6PdiIiIiLzwKlWqZFVWrFgxSpUqxZ9//mkuW7NmDXXq1DEnXwD8/f0pVqwY0dHR5gTMzp07uXbtGp06dbJoMzg4mJUrV7Jp0yYCAwOB+8uO5M6dm6ZNm5rreXh40KxZM5YtW0ZiYiLZs2c3Lw8SEhJisRFvq1atGD16NL/88osSMCIi8tR16dIFb29v8+dVv379iImJYdmyZQDUqlWL999/P9P6a9iwIUWLFmXWrFl06NCBxYsXM3nyZJYtW8a1a9fInTs3pUuX5t133zWf4+Liwrx58wgPD2ft2rUsW7YMDw8PXnvtNYoWLQqAs7Mzc+bMYcKECaxcuZIbN25QtGhRPvvss0xNIKWkWrVqjB07lunTpzN69Gg8PT3p0qULL730Eh9++OFDz23ZsiWLFi1i4cKFXL9+nTx58lCnTh369euHq6srAN26dWPDhg3s2LGDxMREChQoQO/evc37v/D/2rv3+J7r///j951ntmxjTA5hGGNjDg3lLBspLL4OY0ZyKCPlU+vDp4OEkoQcy0ZoDkssNIpRDh/lUHIOSfQxYw6b2Wb2/v3h937l7T2HMdbmdr1cXLzfz9fj9Ty8ere9vB+v5/MpaeLEiXrvvfe0ePFiXblyRQ0bNtS8efPuau8es9DQUJUsWVKzZs3SrFmzZGtrq0qVKhn3S5LUsGFDLV68WNOnT9cXX3yhtLQ0eXl5yd/f39hbCACAu3HPCZijR4/e8ibA09OT6ZoAAAB3yWQy6cyZM8YeMElJSTp79myum8QGBARo/fr1xvt9+/ZJklVsrVq1ZGtrq3379hkJmP3798vPz8/YuNjM399fixcv1tGjR+Xn56eDBw8qOzvbqk5HR0fVrFnTaBMAgAepSpUqqlKlivHe2dlZ06ZNU1pammxsbO5qj7LQ0FDj9+SNbG1ttXbtWuN9+fLlNW7cuNvWWaJECY0aNUqjRo26aYynp6fGjBlzy3rKly9vsTzp9W4sHz9+vMaPH28VFxkZqcjISIsy8/4sNzLvdWN244yQ4OBgBQcH37LPgwYN0qBBg24ZU7p0aYtZt2bX399IN/9vExQUlOt1Me9Ncys1atTQlClTbhkDAEBe3XMCxt7e3mqt8eslJSUZTzsAAAAgb+Lj45WUlGTsr3L69GlJkpeXl1Wsl5eX0tLSlJ6eLhcXF2O5kNKlS1vEOTo6yt3d3ahLkpKTkxUYGGhVp/nc06dPy8/Pz6jzZu2bNwm+Fzk5OTpw4MA912PeeBfIb+np6Tp+/HhBd8PAZx33S3581nNycqyS+/fDTz/9JB8fH2NpTTPz9xEpKSk6cuSIGjZseN/7AgAAYHbPd0F+fn7asGFDrseys7O1cuVK1alT516bAQAAeOgcOXJEo0ePVt26dY0nT80Pvly/T4yZk5OTJBmbzGZkZMjBwUE2Nja5xl7/EE1GRkaudZrLrq/zVu3f6sEcAADul/DwcG3evPmmx//73/8qPDz8AfYIAAAgH2bA9OrVS8OGDdO4ceOMLwauXLmi/fv3a+LEifrjjz/0n//85547CgAA8DBJTk7WwIED5ebmpilTpsjOzk7S30mWrKwsq3PMyQ9nZ2fj7ytXruT69HFmZqZRlzk2tzrNZdfXeav2r6/zbtna2qpGjRr3XM81aflUD/A3FxeXfPyM5o+Mgu4AiqT8+Kzv2rUrn3pzayaT6ZbHr169+kBm4gAAAFzvnhMwwcHBevHFFzVjxgx9/vnnkqSBAwdKunYD9PLLL+uJJ56412YAAAAeGqmpqXrhhReUmpqqhQsXqkyZMsYx85Jg5qXArpecnCxXV1e5uLhI+nuZsOTkZIs6srKydP78eYulyby8vHKt07xMmTn2+jpza//G5c4AAHhQcpvxabZr1y55eHg8wN4Af7OxsZH/1HcKuhsFrrgPS2YCePjcUwImPT1d0dHRCgwM1LJlyxQfH6+jR48qJydHlSpVUseOHXPdIBYAAAC5y8zM1KBBg3Ts2DHFxMSoatWqFsfLlCkjT09P7dmzx+rc3bt3q2bNmsZ78+s9e/ZYJGD27NmjnJwci9gaNWroxx9/tJots3v3bjk5ORkbG1evXl329vbas2ePnnnmGSMuKytL+/fvV9u2be/xCgAAcGfmzZtnPAgqSWPHjtWkSZOs4i5evKi0tDSrjeSBB8UkqUTAP2vm5IOSnp4uScYDQgDwsLmnBIyLi4tmzpypt956S02bNrX4RzwAAADy5urVq3r55Zf1888/a/r06QoMDMw1rm3btlq2bJlOnjypcuXKSZK2bt2qY8eOqXfv3kZco0aN5O7urtjYWLVu3dooj42NlZOTk1q2bGmUhYSEaM2aNUpISFD79u0lXduwOCEhQS1atDCWHnNzc1Pjxo21cuVKRUZGGpsbr1ixQunp6QoJCcnfiwIAwE088sgjevTRRyVJf/31l9zd3VWyZEmLGBsbG1WrVk1169ZVREREAfQSuP0SeUXZ8ePHJekft3QnADwo97wEWbly5ZSSkpIffQEAAHiojR8/XuvXr1fLli11/vx5rVixwuJ4x44dJUmDBg1SQkKC+vTpo/DwcGVkZGjOnDmqWrWqunbtasQ7Oztr6NChGj16tCIjI9WsWTNt375d8fHxioyMlKenpxEbHBysunXrauTIkTp69Kg8PDwUGxur7OxsDRs2zKIfw4cPV/fu3dWrVy9169ZNSUlJio6OVqNGjdSiRYv7d4EAALhO586d1blzZ0nXvtwdPHiwxexMAACAgnbPCZjQ0FAtX75cffr0MZ6MBAAAQN4dOHBAkpSYmKjExESr4+YETNmyZbVgwQKNHz9ekyZNkr29vZo1a6aoqCg5OTlZnBMWFiZHR0dFR0crMTFR3t7eioqKsnoK2M7OTrNnz9aECRM0f/58ZWRkyN/fX2PHjpWPj49FbK1atRQTE6OJEydq3LhxcnFxUWhoqEaMGHHL9fcBALhfzL9DAQAA/knuOQETEBCghIQEdezYUb169dJjjz2mYsWKWcU1bNjwXpsCAAAo0ubPn3/HsdWqVdOcOXPuKLZr164WM2NupkSJEhozZozGjBlz29gGDRooNjb2jtoHAOB+q1mzpiZMmKAOHTrkenz16tV69dVXtX///gfcMwAA8DC75wRM3759jdfvvfee1VOPJpNJNjY23OQAAAAAAID7wmQy3XKfjYd5Dw4UPGYIA8DD654TMOPGjcuPfgAAAAAAANwXf/31l4oXL17Q3cBDykZS+m93vkyeU/mKsivmcv86BAB4YO45AWPe8A4AAAAAAOBB+e6777Ru3Trj/ZIlS7RlyxaruAsXLmjr1q2qV6/eg+weYDCZTDo+7j93HF/xjXflUq3GfewRAOBBuecEDAAAAAAAwIN24MABffXVV5KuLfH0008/6aeffrKKc3FxUWBgoN58880H3UUAAPCQIwEDAAAAAAAKnSFDhmjIkCGSpBo1amjChAl65plnCrhXAAAAfyMBAwAAAAAACrXPP/9cPj4+Bd0NAAAACyRgAAAAAABAofb444/r6tWrWrNmjX755RdduHBBOTk5FjE2NjYaO3ZsAfUQAAA8jEjAAAAAAACAQu3ChQuKiIjQgQMHZDKZZGNjI5PJJEnGaxIwAADgQbMt6A4AAAAAAADciylTpui3337Tu+++q2+//VYmk0mfffaZVq1apXbt2ikgIEA//vhjQXcTeXDixAn5+vpq2bJl96X+3r17q3fv3velbgAAzEjAAAAAAACAQi0xMVHPPvusunTpIldXV0mSnZ2dfHx89NFHH8ne3l4ff/xxwXaykIuLi5Ovr6+Cg4MLuisAABQaJGAAAAAAAEChdvr0aQUEBEiS7O2vrbaelZVlHH/qqaf03XffFUjfior4+HiVK1dOx44d0+7duwu6O/dszpw5mjNnTkF3AwBQxJGAAQAAAAAAhZqbm5syMzMlSS4uLrK3t9fp06eN487Ozjp//nwB9a7wO3XqlH766SeNGDFC3t7eio+PL+gu3TNHR0c5OjoWdDcAAEUcCRgAAAAAAFCoVaxYUceOHZN0bemxatWqKSEhQZKUk5OjNWvWqGzZsgXYw8Lt66+/lrOzs1q1aqX27dvrm2++0dWrVy1ifH199eabb+q7775Thw4dVLt2bT399NP6/vvvLeJOnjypd955R+3atVOdOnXUoEEDDRw4UAcPHrxlHzZv3ixfX1+tXbvW6ti6devk6+urjRs3SpLOnDmjkSNHqnnz5qpdu7aaNGmi/v3767fffjPOyW0PmNWrV+u5555TvXr1FBgYqHbt2mnatGl5ulYAAFyPBAwAAAAAACjUGjdurG+//dZICvTo0UObN29WmzZt1LZtW23btk1du3Yt4F4WXvHx8WrdurWcnZ319NNP68yZM9q8ebNV3M8//6x33nlH7du317/+9S9lZmZq6NChOnfunBHz66+/avv27QoODtbIkSMVERGhvXv3qnfv3hazlm7UuHFjlSlTJtfZN/Hx8fLy8tKTTz4pSRo6dKjWrFmjTp066a233lJERIQkGUm63GzZskWvvPKK3Nzc9Oqrr+q1115T06ZNtXPnzju8SgAAWLMv6A4AAAAAAADcixdeeEHPPvusTCaTJOn//u//dPnyZa1YsUK2trbq3r27nn/++QLuZeF04MABHTp0SK+88ookqXbt2qpUqZLi4+PVrFkzi9ijR49q1apVeuyxxyRJQUFB6tixo1atWqVevXpJklq0aKGQkBCL8zp27Kj27dsrLi5OL774Yq79sLW11bPPPqt58+bpwoULKlGihCQpLS1NiYmJ6tGjh+zs7HTx4kXt2LFDr732msV/8wEDBtxynBs2bFDx4sU1Z84c2dnZ5eEKAQBwc8yAAQAAAAAAhVrx4sVVpUoV2dv//Zxpnz59tGzZMsXFxal///4F2LvCLT4+Xu7u7sbsEkl6+umntW7dOqWnp1vEBgUFGckXSapRo4ZcXV31559/GmXOzs7G68uXL+vcuXMqXry4KleurL17996yL507d1ZWVpa++eYboywhIUGZmZnq2LGjUb+Dg4N+/PHHPO374+bmpsuXL2vTpk13fA4AALfDDBgAAAAAAABYycnJ0apVq9SwYUP99ddfRnlAQIDS09P13Xff6dlnnzXKc9tnp0SJErp48aLxPjMzU5MnT1Z8fLySk5MtYt3d3W/ZHx8fH/n7+ys+Pl7du3eXdC1BVK1aNfn5+UmSHB0dNWLECH3wwQd64oknFBAQoObNm6tjx4633AeoZ8+eSkhI0IABA1S6dGk1adJEbdu2VatWrWRjY3PLfgEAcDMkYAAAAAAAAGBl27ZtOnXqlE6dOqVvv/3W6nh8fLxFAsbWNveFVsxLw0nSu+++qy+//FK9evVSYGCgHnnkEdna2mrs2LEWcTfTqVMnjRkzRidOnJC9vb1++uknDR8+3CImIiJCrVu31rp167RlyxZNnz5ds2bN0syZMxUUFJRrvSVLltRXX32lrVu36vvvv9cPP/yg5cuXq2XLlpoxYwZJGADAXSlyCZj4+Hj961//kqOjo3799VeLY0eOHNG4ceO0Y8cOOTg4qGnTpnrjjTdUqlQpq3ri4uIUHR2tP//8U97e3goLC1OfPn2sfuFevHhRH374odauXauMjAz5+/vrtddek7+/v1WdO3fu1Icffqi9e/eqePHiCg4O1ogRI1S8ePH8vQgAAAAAAAD3KD4+Xh4eHnr77betjm3atElfffWVzp49q5IlS95xnQkJCerUqZNGjhxpUX7hwgV5eHjc9vynn35a48ePV3x8vBwcHCTJIglkVqFCBUVERCgiIkKnTp1Sx44db5mAka7NnmnevLmaN28uk8mkiRMn6tNPP9XOnTtVv379Ox4jAABmRSoBc+nSJU2YMEEuLi7Kzs62OHbq1CmFhYXJ1dVVw4cP1+XLlzVnzhwdOnRIcXFxcnJyMmIXLVqkt956S23btlXfvn21fft2jRs3TpcvX9bgwYONuJycHA0YMEAHDx5Uv3795OnpqdjYWIWHhysuLk4+Pj5G7P79+xUREaEqVaro9ddfV1JSkmJiYnTs2DHFxMTc/4sDAAAAAABwhzIzM7V27Vq1adNGISEhVserVaumpUuXatWqVQoPD7/jeu3s7KxmuqxcuVKnT59WpUqVbnu+h4eHWrRoofj4eDk6OiooKEje3t7G8cuXL8vGxsZirxlvb2+VLFnSYim0G507d84iAWRjY2Msa5aamnqnwwMAwEKRSsDMmDFDxYsXV1BQkNasWWNxbObMmbp06ZK+/PJLlStXTpLk7++vvn37Ki4uTmFhYZKkjIwMTZo0SU2bNtXUqVMlSV27dtXVq1c1c+ZMdevWTZ6enpKuPbWxa9cuTZo0Se3bt5cktWvXTsHBwZoyZYomT55stP/RRx/Jzc1N8+fPl5ubmySpfPnyGjVqlDZu3KjmzZvf34sDAAAAAABwh9atW6e0tDS1atUq1+M+Pj6qVKmS4uPj85SAadWqlZYvXy5XV1dVq1ZN+/fv1zfffKMKFSrccR2dOnXSSy+9JEkaP368xbFjx46pT58+CgkJUdWqVeXo6KiNGzfqyJEjev31129a56hRo3Tu3Dk1btxY3t7eSkpK0sKFC+Xl5aUGDRrccd8AALhe7otzFkLHjh3T3Llz9cYbb8je3jqvtHbtWjVv3txIvkhSkyZNVKlSJSUkJBhl27Z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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n", "\n", "sns.barplot(x=[\"normal\", \"malicious\"],\n", " y=train_raw[TARGET].value_counts().sort_index().values, ax=axes[0],\n", " hue=[\"normal\", \"malicious\"], palette=[\"#4C72B0\", \"#C44E52\"], legend=False)\n", "axes[0].set_title(\"Training partition\")\n", "axes[0].set_ylabel(\"records\")\n", "\n", "sns.barplot(x=[\"normal\", \"malicious\"],\n", " y=test_raw[TARGET].value_counts().sort_index().values, ax=axes[1],\n", " hue=[\"normal\", \"malicious\"], palette=[\"#4C72B0\", \"#C44E52\"], legend=False)\n", "axes[1].set_title(\"Testing partition\")\n", "axes[1].set_ylabel(\"\")\n", "\n", "ac = train_raw['attack_cat'].value_counts()\n", "sns.barplot(x=ac.values, y=ac.index, ax=axes[2], hue=ac.index, palette=\"rocket\", legend=False)\n", "axes[2].set_title(\"Attack categories (training partition)\")\n", "axes[2].set_xlabel(\"records\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "05f09d7a", "metadata": { "id": "05f09d7a" }, "source": [ "## Data Preparation\n" ] }, { "cell_type": "code", "execution_count": null, "id": "781a453d", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:14.519823Z", "iopub.status.busy": "2026-09-08T06:38:14.519638Z", "iopub.status.idle": "2026-09-08T06:38:14.777497Z", "shell.execute_reply": "2026-09-08T06:38:14.775653Z" }, "id": "781a453d", "outputId": "7cd13142-08f8-4fc6-f141-9dc1630ec84c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training records before dedupe: 175,341 normal=56,000 malicious=119,341 ratio=2.131\n", "training records after dedupe: 101,269 normal=51,890 malicious=49,379 ratio=0.952\n", "removed: 74,072\n" ] } ], "source": [ "train_clean = train_raw.drop_duplicates(subset=FEATURES + [TARGET]).reset_index(drop=True)\n", "\n", "before = train_raw[TARGET].value_counts().sort_index()\n", "after = train_clean[TARGET].value_counts().sort_index()\n", "print(f\"training records before dedupe: {len(train_raw):>7,} \"\n", " f\"normal={before[0]:>6,} malicious={before[1]:>6,} ratio={before[1]/before[0]:.3f}\")\n", "print(f\"training records after dedupe: {len(train_clean):>7,} \"\n", " f\"normal={after[0]:>6,} malicious={after[1]:>6,} ratio={after[1]/after[0]:.3f}\")\n", "print(f\"removed: {len(train_raw) - len(train_clean):,}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b5219867", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:14.780090Z", "iopub.status.busy": "2026-09-08T06:38:14.779843Z", "iopub.status.idle": "2026-09-08T06:38:15.050139Z", "shell.execute_reply": "2026-09-08T06:38:15.049410Z" }, "id": "b5219867", "outputId": "dc4315fe-cf50-45d1-beb7-e9c61cdd166a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train split : 81,015 records malicious=0.488\n", "val split : 20,254 records malicious=0.488\n", "test set : 82,332 records malicious=0.551 (held out)\n" ] } ], "source": [ "X_full = train_clean[FEATURES].copy()\n", "y_full = train_clean[TARGET].to_numpy()\n", "X_test_df = test_raw[FEATURES].copy()\n", "y_test = test_raw[TARGET].to_numpy()\n", "\n", "X_tr_df, X_val_df, y_tr, y_val = train_test_split(\n", " X_full, y_full, test_size=0.20, stratify=y_full, random_state=RANDOM_STATE)\n", "\n", "print(f\"train split : {X_tr_df.shape[0]:>7,} records malicious={y_tr.mean():.3f}\")\n", "print(f\"val split : {X_val_df.shape[0]:>7,} records malicious={y_val.mean():.3f}\")\n", "print(f\"test set : {X_test_df.shape[0]:>7,} records malicious={y_test.mean():.3f} (held out)\")" ] }, { "cell_type": "code", "execution_count": null, "id": "dfd2203f", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:15.052877Z", "iopub.status.busy": "2026-09-08T06:38:15.052631Z", "iopub.status.idle": "2026-09-08T06:38:16.990815Z", "shell.execute_reply": "2026-09-08T06:38:16.989213Z" }, "id": "dfd2203f", "outputId": "71e1a369-bbaa-4998-af12-2d338b9c501e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "preprocessor fitted on the training split only (1.9s)\n", "train matrix: (81015, 192)\n", "val matrix: (20254, 192)\n", "test matrix: (82332, 192)\n", "encoded features: 192\n" ] } ], "source": [ "preprocessor = ColumnTransformer(\n", " transformers=[\n", " (\"num\", Pipeline([\n", " (\"log\", FunctionTransformer(np.log1p, feature_names_out=\"one-to-one\")),\n", " (\"scale\", StandardScaler()),\n", " ]), NUM_COLS),\n", " (\"cat\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False), CAT_COLS),\n", " ],\n", " remainder=\"drop\",\n", " verbose_feature_names_out=False,\n", ")\n", "\n", "t0 = time.time()\n", "X_tr = preprocessor.fit_transform(X_tr_df).astype(np.float32)\n", "X_val = preprocessor.transform(X_val_df).astype(np.float32)\n", "X_test = preprocessor.transform(X_test_df).astype(np.float32)\n", "feature_names = list(preprocessor.get_feature_names_out())\n", "\n", "print(f\"preprocessor fitted on the training split only ({time.time()-t0:.1f}s)\")\n", "print(f\"train matrix: {X_tr.shape}\")\n", "print(f\"val matrix: {X_val.shape}\")\n", "print(f\"test matrix: {X_test.shape}\")\n", "print(f\"encoded features: {len(feature_names)}\")" ] }, { "cell_type": "markdown", "id": "ab6fe6d8", "metadata": { "id": "ab6fe6d8" }, "source": [ "### Leakage Checks" ] }, { "cell_type": "code", "execution_count": null, "id": "613ae79d", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:16.993606Z", "iopub.status.busy": "2026-09-08T06:38:16.993311Z", "iopub.status.idle": "2026-09-08T06:38:17.059906Z", "shell.execute_reply": "2026-09-08T06:38:17.058750Z" }, "id": "613ae79d", "outputId": "cf643ada-0764-4786-b899-812a7399a7d2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scaler was fitted on n = 81015.0 which equals the training split size: True\n", "training split mean/std of encoded numeric block: -0.0000 / 1.0000\n", "test set mean/std of the same block (must drift, it was not used for fitting): -0.1022 / 1.2696\n", "target columns present in feature matrix: False\n", "NaN / inf in encoded matrices: False\n" ] } ], "source": [ "scaler = preprocessor.named_transformers_[\"num\"].named_steps[\"scale\"]\n", "print(\"scaler was fitted on n =\", scaler.n_samples_seen_,\n", " \"which equals the training split size:\", scaler.n_samples_seen_ == X_tr_df.shape[0])\n", "\n", "print(\"training split mean/std of encoded numeric block:\",\n", " f\"{X_tr[:, :len(NUM_COLS)].mean():.4f} / {X_tr[:, :len(NUM_COLS)].std():.4f}\")\n", "print(\"test set mean/std of the same block (must drift, it was not used for fitting):\",\n", " f\"{X_test[:, :len(NUM_COLS)].mean():.4f} / {X_test[:, :len(NUM_COLS)].std():.4f}\")\n", "\n", "print(\"target columns present in feature matrix:\",\n", " any(n in (\"label\", \"attack_cat\", \"id\") for n in feature_names))\n", "print(\"NaN / inf in encoded matrices:\",\n", " bool(np.isnan(X_tr).any() or np.isnan(X_test).any()\n", " or np.isinf(X_tr).any() or np.isinf(X_test).any()))" ] }, { "cell_type": "markdown", "id": "be7a2639", "metadata": { "id": "be7a2639" }, "source": [ "### Class Imbalance Investigation\n" ] }, { "cell_type": "code", "execution_count": null, "id": "19378205", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:17.062480Z", "iopub.status.busy": "2026-09-08T06:38:17.062169Z", "iopub.status.idle": "2026-09-08T06:38:17.070363Z", "shell.execute_reply": "2026-09-08T06:38:17.069334Z" }, "id": "19378205", "outputId": "be3a0b78-73cf-41e6-ff7e-618087877567" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training split : normal=41,512 malicious=39,503 ratio=0.952\n", "minority class share: 48.8%\n", "\n", "decision: no synthetic resampling applied; class_weight tested empirically in the model grids\n" ] } ], "source": [ "cnt = pd.Series(y_tr).value_counts().sort_index()\n", "ratio = cnt[1] / cnt[0]\n", "print(f\"training split : normal={cnt[0]:,} malicious={cnt[1]:,} ratio={ratio:.3f}\")\n", "print(f\"minority class share: {min(cnt) / cnt.sum():.1%}\")\n", "print()\n", "print(\"decision: no synthetic resampling applied; class_weight tested empirically in the model grids\")" ] }, { "cell_type": "markdown", "id": "eec6d2b2", "metadata": { "id": "eec6d2b2" }, "source": [ "## Modelling\n", "\n", "All three algorithms consume the identical `X_tr` matrix, are selected on the identical `X_val` matrix, and are never exposed to `X_test` before the evaluation stage." ] }, { "cell_type": "markdown", "id": "0a518566", "metadata": { "id": "0a518566" }, "source": [ "### Evaluation Utilities" ] }, { "cell_type": "code", "execution_count": null, "id": "a3612b3f", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:17.072506Z", "iopub.status.busy": "2026-09-08T06:38:17.072269Z", "iopub.status.idle": "2026-09-08T06:38:17.079518Z", "shell.execute_reply": "2026-09-08T06:38:17.078408Z" }, "id": "a3612b3f" }, "outputs": [], "source": [ "def evaluate(y_true, y_pred, y_score=None, name=\"\"):\n", " tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()\n", " res = {\n", " \"model\": name,\n", " \"accuracy\": accuracy_score(y_true, y_pred),\n", " \"precision\": precision_score(y_true, y_pred, zero_division=0),\n", " \"recall\": recall_score(y_true, y_pred, zero_division=0),\n", " \"f1\": f1_score(y_true, y_pred, zero_division=0),\n", " \"tpr\": tp / (tp + fn) if (tp + fn) else 0.0,\n", " \"fpr\": fp / (fp + tn) if (fp + tn) else 0.0,\n", " \"fnr\": fn / (fn + tp) if (fn + tp) else 0.0,\n", " \"specificity\": tn / (tn + fp) if (tn + fp) else 0.0,\n", " \"tn\": int(tn), \"fp\": int(fp), \"fn\": int(fn), \"tp\": int(tp),\n", " }\n", " res[\"roc_auc\"] = roc_auc_score(y_true, y_score) if y_score is not None else np.nan\n", " return res\n", "\n", "\n", "def show(res):\n", " print(f\"{res['model']}\")\n", " print(f\" accuracy {res['accuracy']:.4f} precision {res['precision']:.4f} \"\n", " f\"recall {res['recall']:.4f} f1 {res['f1']:.4f}\")\n", " print(f\" TPR {res['tpr']:.4f} FPR {res['fpr']:.4f} \"\n", " f\"FNR {res['fnr']:.4f} AUC {res['roc_auc']:.4f}\")\n", " print(f\" TN {res['tn']:>6} FP {res['fp']:>6} FN {res['fn']:>6} TP {res['tp']:>6}\")\n", "\n", "\n", "results = {}\n", "artifacts = {}\n", "timings = {}" ] }, { "cell_type": "markdown", "id": "0c56404d", "metadata": { "id": "0c56404d" }, "source": [ "### Random Forest\n", "\n", "A grid over tree depth, leaf size and class weighting is scored on the validation split by F1, so that both false positives and missed attacks influence the choice. `n_estimators` is fixed at 300, which is past the point where additional trees change the validation score materially and keeps the run practical on Colab hardware." ] }, { "cell_type": "code", "execution_count": null, "id": "49a3181b", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:38:17.082211Z", "iopub.status.busy": "2026-09-08T06:38:17.081981Z", "iopub.status.idle": "2026-09-08T06:41:20.931409Z", "shell.execute_reply": "2026-09-08T06:41:20.929945Z" }, "id": "49a3181b", "outputId": "abc3435e-44b3-413c-83ee-c7868dea6885" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "max_depth min_samples_leaf class_weight val_f1 val_recall val_fpr fit_s\n", " None 3 balanced 0.9296 0.9644 0.1051 22.7\n", " None 3 None 0.9295 0.9623 0.1031 22.5\n", " None 1 None 0.9290 0.9477 0.0881 24.3\n", " None 1 balanced 0.9286 0.9483 0.0895 25.1\n", " 20 1 balanced 0.9286 0.9594 0.1018 23.1\n", " 20 1 None 0.9284 0.9566 0.0992 23.0\n", " 20 3 balanced 0.9284 0.9687 0.1124 21.6\n", " 20 3 None 0.9283 0.9653 0.1088 21.5\n", "\n", "selected: None 3 balanced | val f1 = 0.9296\n" ] } ], "source": [ "rf_grid = [\n", " {\"max_depth\": d, \"min_samples_leaf\": l, \"class_weight\": w}\n", " for d in (None, 20) for l in (1, 3) for w in (None, \"balanced\")\n", "]\n", "\n", "rf_rows = []\n", "best_rf, best_rf_f1, val_pred = None, -1.0, {}\n", "for params in rf_grid:\n", " t0 = time.time()\n", " m = RandomForestClassifier(n_estimators=300, n_jobs=-1,\n", " random_state=RANDOM_STATE, **params).fit(X_tr, y_tr)\n", " p = m.predict(X_val)\n", " f1v = f1_score(y_val, p)\n", " tn, fp, fn, tp = confusion_matrix(y_val, p, labels=[0, 1]).ravel()\n", " rf_rows.append({**{k: str(v) for k, v in params.items()},\n", " \"val_f1\": round(f1v, 4),\n", " \"val_recall\": round(recall_score(y_val, p), 4),\n", " \"val_fpr\": round(fp / (fp + tn), 4),\n", " \"fit_s\": round(time.time() - t0, 1)})\n", " if f1v > best_rf_f1:\n", " best_rf, best_rf_f1 = m, f1v\n", " val_pred[\"Random Forest\"] = p\n", "\n", "rf_search = pd.DataFrame(rf_rows).sort_values(\"val_f1\", ascending=False)\n", "print(rf_search.to_string(index=False))\n", "print(\"\\nselected:\", best_rf.get_params()[\"max_depth\"], best_rf.get_params()[\"min_samples_leaf\"],\n", " best_rf.get_params()[\"class_weight\"], f\"| val f1 = {best_rf_f1:.4f}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b8126991", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:41:20.934649Z", "iopub.status.busy": "2026-09-08T06:41:20.934415Z", "iopub.status.idle": "2026-09-08T06:41:45.202495Z", "shell.execute_reply": "2026-09-08T06:41:45.201382Z" }, "id": "b8126991", "outputId": "9c067707-02b1-41fd-f8e2-3c3c1da742fe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random Forest\n", " accuracy 0.8612 precision 0.8028 recall 0.9916 f1 0.8872\n", " TPR 0.9916 FPR 0.2985 FNR 0.0084 AUC 0.9824\n", " TN 25956 FP 11044 FN 381 TP 44951\n" ] } ], "source": [ "t0 = time.time()\n", "rf_final = RandomForestClassifier(\n", " n_estimators=300, n_jobs=-1, random_state=RANDOM_STATE,\n", " max_depth=best_rf.get_params()[\"max_depth\"],\n", " min_samples_leaf=best_rf.get_params()[\"min_samples_leaf\"],\n", " class_weight=best_rf.get_params()[\"class_weight\"]).fit(X_tr, y_tr)\n", "timings[\"Random Forest\"] = {\"train_s\": time.time() - t0}\n", "\n", "t0 = time.time()\n", "rf_pred = rf_final.predict(X_test)\n", "timings[\"Random Forest\"][\"predict_s\"] = time.time() - t0\n", "rf_score = rf_final.predict_proba(X_test)[:, 1]\n", "\n", "results[\"Random Forest\"] = evaluate(y_test, rf_pred, rf_score, \"Random Forest\")\n", "artifacts[\"Random Forest\"] = rf_final\n", "show(results[\"Random Forest\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "9c72d3bc", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:41:45.205501Z", "iopub.status.busy": "2026-09-08T06:41:45.205250Z", "iopub.status.idle": "2026-09-08T06:41:45.527505Z", "shell.execute_reply": "2026-09-08T06:41:45.526290Z" }, "id": "9c72d3bc", "outputId": "c9e0020a-aa21-408e-867d-1760dc24b543" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "imp = pd.Series(rf_final.feature_importances_, index=feature_names).nlargest(15)\n", "plt.figure(figsize=(7, 5))\n", "sns.barplot(x=imp.values, y=imp.index, hue=imp.index, palette=\"viridis\", legend=False)\n", "plt.title(\"Random Forest feature importance (top 15)\")\n", "plt.xlabel(\"mean decrease in impurity\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8dcfbc10", "metadata": { "id": "8dcfbc10" }, "source": [ "### Support Vector Machine\n", "\n", "An RBF kernel is used because the class boundary in this feature space is not linearly separable. The SVM depends on feature scale, which the shared pipeline already provides. `gamma` is left at `scale`, and `C` and class weighting are searched on the validation split. Kernel SVM training cost grows roughly quadratically with the number of records, so the grid is deliberately small; removing duplicate records earlier is what keeps a full RBF fit on the complete training split feasible within Colab runtimes rather than forcing a subsample." ] }, { "cell_type": "code", "execution_count": null, "id": "86212f0f", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T06:41:45.529879Z", "iopub.status.busy": "2026-09-08T06:41:45.529646Z", "iopub.status.idle": "2026-09-08T07:05:53.909411Z", "shell.execute_reply": "2026-09-08T07:05:53.907904Z" }, "id": "86212f0f", "outputId": "a195046f-c86c-4d92-c7e3-b89ae1e51e90" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'C': 1.0, 'class_weight': 'None', 'val_f1': 0.9155, 'val_recall': 0.9721, 'val_fpr': np.float64(0.1442), 'n_support': 16710, 'fit_s': 313.1}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'C': 1.0, 'class_weight': 'balanced', 'val_f1': 0.9151, 'val_recall': 0.9804, 'val_fpr': np.float64(0.1545), 'n_support': 16491, 'fit_s': 330.1}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'C': 10.0, 'class_weight': 'None', 'val_f1': 0.9205, 'val_recall': 0.9578, 'val_fpr': np.float64(0.1172), 'n_support': 15694, 'fit_s': 282.7}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'C': 10.0, 'class_weight': 'balanced', 'val_f1': 0.9212, 'val_recall': 0.9649, 'val_fpr': np.float64(0.1237), 'n_support': 15567, 'fit_s': 311.5}\n", "\n", " C class_weight val_f1 val_recall val_fpr n_support fit_s\n", "10.0 balanced 0.9212 0.9649 0.1237 15567 311.5\n", "10.0 None 0.9205 0.9578 0.1172 15694 282.7\n", " 1.0 None 0.9155 0.9721 0.1442 16710 313.1\n", " 1.0 balanced 0.9151 0.9804 0.1545 16491 330.1\n", "\n", "selected: C = 10.0 | class_weight = balanced | val f1 = 0.9212\n" ] } ], "source": [ "svm_grid = [{\"C\": c, \"class_weight\": w} for c in (1.0, 10.0) for w in (None, \"balanced\")]\n", "\n", "svm_rows = []\n", "best_svm, best_svm_f1 = None, -1.0\n", "for params in svm_grid:\n", " t0 = time.time()\n", " m = SVC(kernel=\"rbf\", gamma=\"scale\", cache_size=1000,\n", " random_state=RANDOM_STATE, **params).fit(X_tr, y_tr)\n", " fit_s = time.time() - t0\n", " p = m.predict(X_val)\n", " f1v = f1_score(y_val, p)\n", " tn, fp, fn, tp = confusion_matrix(y_val, p, labels=[0, 1]).ravel()\n", " svm_rows.append({\"C\": params[\"C\"], \"class_weight\": str(params[\"class_weight\"]),\n", " \"val_f1\": round(f1v, 4),\n", " \"val_recall\": round(recall_score(y_val, p), 4),\n", " \"val_fpr\": round(fp / (fp + tn), 4),\n", " \"n_support\": int(m.n_support_.sum()),\n", " \"fit_s\": round(fit_s, 1)})\n", " print(svm_rows[-1])\n", " if f1v > best_svm_f1:\n", " best_svm, best_svm_f1 = m, f1v\n", " val_pred[\"Support Vector Machine\"] = p\n", "\n", "svm_search = pd.DataFrame(svm_rows).sort_values(\"val_f1\", ascending=False)\n", "print()\n", "print(svm_search.to_string(index=False))\n", "print(\"\\nselected: C =\", best_svm.C, \"| class_weight =\", best_svm.class_weight,\n", " f\"| val f1 = {best_svm_f1:.4f}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "30ad8624", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:05:53.912499Z", "iopub.status.busy": "2026-09-08T07:05:53.912187Z", "iopub.status.idle": "2026-09-08T07:12:59.671693Z", "shell.execute_reply": "2026-09-08T07:12:59.669819Z" }, "id": "30ad8624", "outputId": "12e639a6-82a9-47ee-cd1b-e8c65bb46979" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Support Vector Machine\n", " accuracy 0.8413 precision 0.7833 recall 0.9840 f1 0.8722\n", " TPR 0.9840 FPR 0.3335 FNR 0.0160 AUC 0.9527\n", " TN 24659 FP 12341 FN 726 TP 44606\n" ] } ], "source": [ "svm_final = best_svm\n", "timings[\"Support Vector Machine\"] = {\n", " \"train_s\": float(svm_search.iloc[0][\"fit_s\"])}\n", "\n", "t0 = time.time()\n", "svm_pred = svm_final.predict(X_test)\n", "timings[\"Support Vector Machine\"][\"predict_s\"] = time.time() - t0\n", "\n", "svm_score = svm_final.decision_function(X_test)\n", "\n", "results[\"Support Vector Machine\"] = evaluate(y_test, svm_pred, svm_score, \"Support Vector Machine\")\n", "artifacts[\"Support Vector Machine\"] = svm_final\n", "show(results[\"Support Vector Machine\"])" ] }, { "cell_type": "markdown", "id": "d9891941", "metadata": { "id": "d9891941" }, "source": [ "### Artificial Neural Network\n", "\n", "A feedforward network for binary classification: ReLU hidden layers with dropout regularisation, a single sigmoid output unit, binary cross-entropy loss and the Adam optimiser. The validation split drives early stopping and learning-rate reduction, so training length is decided by validation loss rather than by a fixed epoch count, and the best weights are restored. Two depths are compared under identical training controls and the better validation F1 is kept." ] }, { "cell_type": "code", "execution_count": null, "id": "f4d21111", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:12:59.674739Z", "iopub.status.busy": "2026-09-08T07:12:59.674441Z", "iopub.status.idle": "2026-09-08T07:16:39.237857Z", "shell.execute_reply": "2026-09-08T07:16:39.236276Z" }, "id": "f4d21111", "outputId": "1fe2433e-620d-40dd-caec-3367bf47a94e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'architecture': '(64, 32)', 'epochs_run': 67, 'best_val_loss': 0.1586, 'val_f1': 0.9229, 'fit_s': 84.5}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'architecture': '(128, 64, 32)', 'epochs_run': 76, 'best_val_loss': 0.154, 'val_f1': 0.922, 'fit_s': 132.8}\n", "\n", " architecture epochs_run best_val_loss val_f1 fit_s\n", " (64, 32) 67 0.1586 0.9229 84.5\n", "(128, 64, 32) 76 0.1540 0.9220 132.8\n" ] }, { "data": { "text/html": [ "
Model: \"ann_64_32\"\n",
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              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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              "│ (BatchNormalization)            │                        │               │\n",
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patience=10,\n", " restore_best_weights=True, verbose=0),\n", " keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5,\n", " patience=4, min_lr=1e-5, verbose=0),\n", " ]\n", " t0 = time.time()\n", " hist = model.fit(X_tr, y_tr, validation_data=(X_val, y_val),\n", " epochs=150, batch_size=256, callbacks=cbs, verbose=0)\n", " fit_s = time.time() - t0\n", " pv = (model.predict(X_val, verbose=0).ravel() >= 0.5).astype(int)\n", " return model, hist, f1_score(y_val, pv), fit_s\n", "\n", "\n", "ann_rows, best_ann, best_ann_hist, best_ann_f1, best_ann_fit = [], None, None, -1.0, 0.0\n", "for hidden in [(64, 32), (128, 64, 32)]:\n", " model, hist, f1v, fit_s = train_ann(hidden)\n", " ann_rows.append({\"architecture\": str(hidden), \"epochs_run\": len(hist.history[\"loss\"]),\n", " \"best_val_loss\": round(min(hist.history[\"val_loss\"]), 4),\n", " \"val_f1\": round(f1v, 4), \"fit_s\": round(fit_s, 1)})\n", " print(ann_rows[-1])\n", " if f1v > best_ann_f1:\n", " best_ann, best_ann_hist, best_ann_f1, best_ann_fit = model, hist, f1v, fit_s\n", "\n", "ann_search = pd.DataFrame(ann_rows)\n", "print()\n", "print(ann_search.to_string(index=False))\n", "best_ann.summary()" ] }, { "cell_type": "code", "execution_count": null, "id": "7e6f6b39", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:39.241572Z", "iopub.status.busy": "2026-09-08T07:16:39.241226Z", "iopub.status.idle": "2026-09-08T07:16:39.516340Z", "shell.execute_reply": "2026-09-08T07:16:39.515248Z" }, "id": "7e6f6b39", "outputId": "5b614575-9e08-49fd-cc13-3da21aaefa6a" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "final train-validation accuracy gap: -0.0054\n", "epochs run: 67 (early stopping restored the best-validation-loss weights)\n" ] } ], "source": [ "h = best_ann_hist.history\n", "fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n", "axes[0].plot(h[\"loss\"], label=\"train\")\n", "axes[0].plot(h[\"val_loss\"], label=\"validation\")\n", "axes[0].set_title(\"ANN loss\"); axes[0].set_xlabel(\"epoch\"); axes[0].set_ylabel(\"binary cross-entropy\")\n", "axes[0].legend()\n", "axes[1].plot(h[\"accuracy\"], label=\"train\")\n", "axes[1].plot(h[\"val_accuracy\"], label=\"validation\")\n", "axes[1].set_title(\"ANN accuracy\"); axes[1].set_xlabel(\"epoch\"); axes[1].set_ylabel(\"accuracy\")\n", "axes[1].legend()\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "gap = h[\"accuracy\"][-1] - h[\"val_accuracy\"][-1]\n", "print(f\"final train-validation accuracy gap: {gap:+.4f}\")\n", "print(f\"epochs run: {len(h['loss'])} (early stopping restored the best-validation-loss weights)\")" ] }, { "cell_type": "code", "execution_count": null, "id": "4c76efb1", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:39.519194Z", "iopub.status.busy": "2026-09-08T07:16:39.518948Z", "iopub.status.idle": "2026-09-08T07:16:44.168603Z", "shell.execute_reply": "2026-09-08T07:16:44.167011Z" }, "id": "4c76efb1", "outputId": "6d190f3c-b51b-4055-abb7-8525b284b50b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Artificial Neural Network\n", " accuracy 0.8624 precision 0.8071 recall 0.9856 f1 0.8875\n", " TPR 0.9856 FPR 0.2885 FNR 0.0144 AUC 0.9827\n", " TN 26324 FP 10676 FN 655 TP 44677\n" ] } ], "source": [ "ann_final = best_ann\n", "val_pred[\"Artificial Neural Network\"] = (ann_final.predict(X_val, verbose=0).ravel() >= 0.5).astype(int)\n", "timings[\"Artificial Neural Network\"] = {\"train_s\": best_ann_fit}\n", "\n", "t0 = time.time()\n", "ann_score = ann_final.predict(X_test, verbose=0).ravel()\n", "timings[\"Artificial Neural Network\"][\"predict_s\"] = time.time() - t0\n", "ann_pred = (ann_score >= 0.5).astype(int)\n", "\n", "results[\"Artificial Neural Network\"] = evaluate(y_test, ann_pred, ann_score,\n", " \"Artificial Neural Network\")\n", "artifacts[\"Artificial Neural Network\"] = ann_final\n", "show(results[\"Artificial Neural Network\"])" ] }, { "cell_type": "markdown", "id": "64c7c812", "metadata": { "id": "64c7c812" }, "source": [ "## Model Evaluation\n", "\n", "Every model is scored once on the untouched testing partition using the metrics required by Objective 4, extended with the false positive measures required by the aim and scope." ] }, { "cell_type": "code", "execution_count": null, "id": "917a537b", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.171176Z", "iopub.status.busy": "2026-09-08T07:16:44.170874Z", "iopub.status.idle": "2026-09-08T07:16:44.261218Z", "shell.execute_reply": "2026-09-08T07:16:44.259821Z" }, "id": "917a537b", "outputId": "6578af38-a789-42ab-d682-9172a45a1556" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "Random Forest\n", " precision recall f1-score support\n", "\n", " normal 0.9855 0.7015 0.8196 37000\n", " malicious 0.8028 0.9916 0.8872 45332\n", "\n", " accuracy 0.8612 82332\n", " macro avg 0.8942 0.8466 0.8534 82332\n", "weighted avg 0.8849 0.8612 0.8569 82332\n", "\n", "======================================================================\n", "Support Vector Machine\n", " precision recall f1-score support\n", "\n", " normal 0.9714 0.6665 0.7905 37000\n", " malicious 0.7833 0.9840 0.8722 45332\n", "\n", " accuracy 0.8413 82332\n", " macro avg 0.8773 0.8252 0.8314 82332\n", "weighted avg 0.8678 0.8413 0.8355 82332\n", "\n", "======================================================================\n", "Artificial Neural Network\n", " precision recall f1-score support\n", "\n", " normal 0.9757 0.7115 0.8229 37000\n", " malicious 0.8071 0.9856 0.8875 45332\n", "\n", " accuracy 0.8624 82332\n", " macro avg 0.8914 0.8485 0.8552 82332\n", "weighted avg 0.8829 0.8624 0.8584 82332\n", "\n" ] } ], "source": [ "preds = {\"Random Forest\": rf_pred,\n", " \"Support Vector Machine\": svm_pred,\n", " \"Artificial Neural Network\": ann_pred}\n", "scores = {\"Random Forest\": rf_score,\n", " \"Support Vector Machine\": svm_score,\n", " \"Artificial Neural Network\": ann_score}\n", "\n", "for name in preds:\n", " print(\"=\" * 70)\n", " print(name)\n", " print(classification_report(y_test, preds[name], target_names=[\"normal\", \"malicious\"],\n", " digits=4, zero_division=0))" ] }, { "cell_type": "code", "execution_count": null, "id": "9fabff4d", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.263942Z", "iopub.status.busy": "2026-09-08T07:16:44.263661Z", "iopub.status.idle": "2026-09-08T07:16:44.551387Z", "shell.execute_reply": "2026-09-08T07:16:44.550204Z" }, "id": "9fabff4d", "outputId": "0ded349d-9acd-4267-9ab4-733a1840a2d2" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 3, figsize=(15, 4.2))\n", "for ax, (name, p) in zip(axes, preds.items()):\n", " cm = confusion_matrix(y_test, p, labels=[0, 1])\n", " sns.heatmap(cm, annot=True, fmt=\",d\", cmap=\"Blues\", cbar=False, ax=ax,\n", " xticklabels=[\"normal\", \"malicious\"], yticklabels=[\"normal\", \"malicious\"])\n", " ax.set_title(f\"{name}\\nFPR = {results[name]['fpr']:.4f} recall = {results[name]['recall']:.4f}\")\n", " ax.set_xlabel(\"predicted\"); ax.set_ylabel(\"actual\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "db1447e6", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.553602Z", "iopub.status.busy": "2026-09-08T07:16:44.553365Z", "iopub.status.idle": "2026-09-08T07:16:44.730664Z", "shell.execute_reply": "2026-09-08T07:16:44.729407Z" }, "id": "db1447e6", "outputId": "d5921fc9-fa2e-4958-c8c4-ac46f0756f19" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6.2, 5.2))\n", "for name, s in scores.items():\n", " fpr, tpr, _ = roc_curve(y_test, s)\n", " plt.plot(fpr, tpr, label=f\"{name} (AUC = {results[name]['roc_auc']:.4f})\")\n", "plt.plot([0, 1], [0, 1], \"k--\", lw=0.8)\n", "plt.xlabel(\"false positive rate\"); plt.ylabel(\"true positive rate\")\n", "plt.title(\"ROC curves on the testing partition\")\n", "plt.legend(loc=\"lower right\", fontsize=8)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "255779d7", "metadata": { "id": "255779d7" }, "source": [ "### Validation Against Test Performance\n", "\n", "The validation split is drawn from the training partition, while the testing partition is a separately constructed file. Comparing the same metrics on both makes visible how much of the performance drop is generalisation error rather than model weakness, which matters when interpreting the false positive figures reported above." ] }, { "cell_type": "code", "execution_count": null, "id": "2c199af8", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.733864Z", "iopub.status.busy": "2026-09-08T07:16:44.733623Z", "iopub.status.idle": "2026-09-08T07:16:44.797842Z", "shell.execute_reply": "2026-09-08T07:16:44.796211Z" }, "id": "2c199af8", "outputId": "80d99966-341d-45c4-bd81-faaec8d976b1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " val_f1 test_f1 val_fpr test_fpr val_recall test_recall f1_drop fpr_increase\n", "model \n", "Random Forest 0.9296 0.8872 0.1051 0.2985 0.9644 0.9916 0.0423 0.1934\n", "Support Vector Machine 0.9212 0.8722 0.1237 0.3335 0.9649 0.9840 0.0489 0.2098\n", "Artificial Neural Network 0.9229 0.8875 0.1046 0.2885 0.9510 0.9856 0.0354 0.1839\n", "\n", "The two published partitions do not share an identical traffic distribution, so the\n", "false positive rate rises on the testing partition even though recall is retained.\n" ] } ], "source": [ "gap_rows = []\n", "for name in val_pred:\n", " v = evaluate(y_val, val_pred[name], None, name)\n", " t = results[name]\n", " gap_rows.append({\"model\": name,\n", " \"val_f1\": round(v[\"f1\"], 4), \"test_f1\": round(t[\"f1\"], 4),\n", " \"val_fpr\": round(v[\"fpr\"], 4), \"test_fpr\": round(t[\"fpr\"], 4),\n", " \"val_recall\": round(v[\"recall\"], 4), \"test_recall\": round(t[\"recall\"], 4),\n", " \"f1_drop\": round(v[\"f1\"] - t[\"f1\"], 4),\n", " \"fpr_increase\": round(t[\"fpr\"] - v[\"fpr\"], 4)})\n", "gap_table = pd.DataFrame(gap_rows).set_index(\"model\")\n", "print(gap_table.to_string())\n", "print()\n", "print(\"The two published partitions do not share an identical traffic distribution, so the\")\n", "print(\"false positive rate rises on the testing partition even though recall is retained.\")" ] }, { "cell_type": "markdown", "id": "c46d120e", "metadata": { "id": "c46d120e" }, "source": [ "## Model Comparison" ] }, { "cell_type": "code", "execution_count": null, "id": "96b59c9a", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.800566Z", "iopub.status.busy": "2026-09-08T07:16:44.800328Z", "iopub.status.idle": "2026-09-08T07:16:44.816844Z", "shell.execute_reply": "2026-09-08T07:16:44.815744Z" }, "id": "96b59c9a", "outputId": "bb73541e-9e13-427d-9dc6-93703412c5bc" }, "outputs": [ { "data": { "text/html": [ "
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accuracyprecisionrecallf1fprfnrroc_auctnfpfntptrain_spredict_sflows_per_s
model
Random Forest0.86120.80280.99160.88720.29850.00840.982425956110443814495121.91.1869926
Support Vector Machine0.84130.78330.98400.87220.33350.01600.9527246591234172644606311.5211.95388
Artificial Neural Network0.86240.80710.98560.88750.28850.01440.982726324106766554467784.53.2425400
\n", "
" ], "text/plain": [ " accuracy precision recall f1 fpr fnr roc_auc tn fp fn tp train_s predict_s flows_per_s\n", "model \n", "Random Forest 0.8612 0.8028 0.9916 0.8872 0.2985 0.0084 0.9824 25956 11044 381 44951 21.9 1.18 69926\n", "Support Vector Machine 0.8413 0.7833 0.9840 0.8722 0.3335 0.0160 0.9527 24659 12341 726 44606 311.5 211.95 388\n", "Artificial Neural Network 0.8624 0.8071 0.9856 0.8875 0.2885 0.0144 0.9827 26324 10676 655 44677 84.5 3.24 25400" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "comparison = pd.DataFrame(results.values())[\n", " [\"model\", \"accuracy\", \"precision\", \"recall\", \"f1\", \"fpr\", \"fnr\", \"roc_auc\",\n", " \"tn\", \"fp\", \"fn\", \"tp\"]].set_index(\"model\").round(4)\n", "comparison[\"train_s\"] = [round(timings[m][\"train_s\"], 1) for m in comparison.index]\n", "comparison[\"predict_s\"] = [round(timings[m][\"predict_s\"], 2) for m in comparison.index]\n", "comparison[\"flows_per_s\"] = [round(len(y_test) / max(timings[m][\"predict_s\"], 1e-6))\n", " for m in comparison.index]\n", "comparison" ] }, { "cell_type": "code", "execution_count": null, "id": "922267a9", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:44.819126Z", "iopub.status.busy": "2026-09-08T07:16:44.818828Z", "iopub.status.idle": "2026-09-08T07:16:45.104097Z", "shell.execute_reply": "2026-09-08T07:16:45.102935Z" }, "id": "922267a9", "outputId": "99b60935-6d6c-4aa2-a563-6516c1371899" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "metrics = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n", "melted = comparison.reset_index().melt(id_vars=\"model\", value_vars=metrics,\n", " var_name=\"metric\", value_name=\"score\")\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4.4))\n", "sns.barplot(data=melted, x=\"metric\", y=\"score\", hue=\"model\", ax=axes[0], palette=\"Set2\")\n", "axes[0].set_ylim(min(melted[\"score\"]) - 0.05, 1.0)\n", "axes[0].set_title(\"Required evaluation metrics on the testing partition\")\n", "axes[0].legend(fontsize=8, loc=\"lower right\")\n", "\n", "fp_df = comparison.reset_index()[[\"model\", \"fpr\", \"fnr\"]].melt(\n", " id_vars=\"model\", var_name=\"error type\", value_name=\"rate\")\n", "sns.barplot(data=fp_df, x=\"error type\", y=\"rate\", hue=\"model\", ax=axes[1], palette=\"Set2\")\n", "axes[1].set_title(\"Error rates (lower is better)\")\n", "axes[1].legend(fontsize=8)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4af9dbb2", "metadata": { "id": "4af9dbb2" }, "source": [ "### Robustness on Records Not Present in Training\n", "\n", "Roughly a tenth of the testing partition consists of flow records whose feature vector also occurs in the training partition, which is a known property of the public UNSW-NB15 split and not something this implementation can remove without abandoning the standard benchmark. Metrics are therefore also reported on the subset of the testing partition that does not occur in training, as a check that the ranking of the models is not an artefact of repeated records." ] }, { "cell_type": "code", "execution_count": null, "id": "41abd62d", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:45.107027Z", "iopub.status.busy": "2026-09-08T07:16:45.106770Z", "iopub.status.idle": "2026-09-08T07:16:45.469826Z", "shell.execute_reply": "2026-09-08T07:16:45.468802Z" }, "id": "41abd62d", "outputId": "90f9b02d-2ee1-41ee-fd63-6aeac5f4991b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testing records not present in training: 73,791 of 82,332 (89.6%) malicious share = 0.519\n" ] }, { "data": { "text/html": [ "
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accuracyprecisionrecallf1fprroc_auc
model
Random Forest0.85250.78290.99030.87440.29580.9798
Support Vector Machine0.83080.76140.98120.85740.33120.9504
Artificial Neural Network0.85420.78820.98300.87490.28440.9799
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" ], "text/plain": [ " accuracy precision recall f1 fpr roc_auc\n", "model \n", "Random Forest 0.8525 0.7829 0.9903 0.8744 0.2958 0.9798\n", "Support Vector Machine 0.8308 0.7614 0.9812 0.8574 0.3312 0.9504\n", "Artificial Neural Network 0.8542 0.7882 0.9830 0.8749 0.2844 0.9799" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unseen_mask = ~key_te.isin(set(key_tr)).to_numpy()\n", "print(f\"testing records not present in training: {unseen_mask.sum():,} of {len(y_test):,}\"\n", " f\" ({unseen_mask.mean():.1%}) malicious share = {y_test[unseen_mask].mean():.3f}\")\n", "\n", "robust = pd.DataFrame([\n", " evaluate(y_test[unseen_mask], preds[n][unseen_mask], scores[n][unseen_mask], n)\n", " for n in preds\n", "])[[\"model\", \"accuracy\", \"precision\", \"recall\", \"f1\", \"fpr\", \"roc_auc\"]].set_index(\"model\").round(4)\n", "robust" ] }, { "cell_type": "markdown", "id": "037b734f", "metadata": { "id": "037b734f" }, "source": [ "### Model Selection\n", "\n", "Selection follows the research aim rather than accuracy alone. The decision rule is stated before it is applied, so that it is not chosen after seeing which model it favours:\n", "\n", "1. Rank by F1-score, because it summarises the balance between missed attacks and false alarms.\n", "2. If the leading models are separated by less than 0.005 F1, treat that difference as not material and break the tie on false positive rate, which the aim targets directly.\n", "3. Report inference throughput as a secondary deployment consideration, since the aim also refers to real time detection." ] }, { "cell_type": "code", "execution_count": null, "id": "208d8a88", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:45.473088Z", "iopub.status.busy": "2026-09-08T07:16:45.472767Z", "iopub.status.idle": "2026-09-08T07:16:45.488130Z", "shell.execute_reply": "2026-09-08T07:16:45.486133Z" }, "id": "208d8a88", "outputId": "6e359255-b8ad-467d-9fc9-6e5be1ac7c50" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ranking by F1-score:\n", "model\n", "Artificial Neural Network 0.8875\n", "Random Forest 0.8872\n", "Support Vector Machine 0.8722\n", "\n", "ranking by false positive rate (ascending):\n", "model\n", "Artificial Neural Network 0.2885\n", "Random Forest 0.2985\n", "Support Vector Machine 0.3335\n", "\n", "ranking by recall:\n", "model\n", "Random Forest 0.9916\n", "Artificial Neural Network 0.9856\n", "Support Vector Machine 0.9840\n", "\n", "ranking by inference throughput (flow records per second):\n", "model\n", "Random Forest 69926\n", "Artificial Neural Network 25400\n", "Support Vector Machine 388\n", "\n", "----------------------------------------------------------------------\n", "selected model for the IDS prototype: Artificial Neural Network\n", "rationale: F1 separates the leading models by only 0.0003, below the 0.005 materiality threshold, so the tie is broken on false positive rate among ['Artificial Neural Network', 'Random Forest']\n", "----------------------------------------------------------------------\n", "accuracy 0.8624\n", "precision 0.8071\n", "recall 0.9856\n", "f1 0.8875\n", "fpr 0.2885\n", "false_alarms 10676.0000\n", "missed_attacks 655.0000\n", "flows_per_s 25400.0000\n" ] } ], "source": [ "sel = comparison[[\"accuracy\", \"precision\", \"recall\", \"f1\", \"fpr\"]].copy()\n", "sel[\"false_alarms\"] = comparison[\"fp\"]\n", "sel[\"missed_attacks\"] = comparison[\"fn\"]\n", "sel[\"flows_per_s\"] = comparison[\"flows_per_s\"]\n", "\n", "print(\"ranking by F1-score:\")\n", "print(sel[\"f1\"].sort_values(ascending=False).to_string())\n", "print(\"\\nranking by false positive rate (ascending):\")\n", "print(sel[\"fpr\"].sort_values().to_string())\n", "print(\"\\nranking by recall:\")\n", "print(sel[\"recall\"].sort_values(ascending=False).to_string())\n", "print(\"\\nranking by inference throughput (flow records per second):\")\n", "print(sel[\"flows_per_s\"].sort_values(ascending=False).to_string())\n", "\n", "F1_TOLERANCE = 0.005\n", "ranked = sel.sort_values(\"f1\", ascending=False)\n", "lead_gap = ranked[\"f1\"].iloc[0] - ranked[\"f1\"].iloc[1]\n", "\n", "if lead_gap < F1_TOLERANCE:\n", " contenders = ranked[ranked[\"f1\"] >= ranked[\"f1\"].iloc[0] - F1_TOLERANCE]\n", " SELECTED = contenders[\"fpr\"].idxmin()\n", " rationale = (f\"F1 separates the leading models by only {lead_gap:.4f}, below the \"\n", " f\"{F1_TOLERANCE} materiality threshold, so the tie is broken on false \"\n", " f\"positive rate among {list(contenders.index)}\")\n", "else:\n", " SELECTED = ranked.index[0]\n", " rationale = f\"highest F1-score by a margin of {lead_gap:.4f}\"\n", "\n", "selected_model = artifacts[SELECTED]\n", "print(\"\\n\" + \"-\" * 70)\n", "print(\"selected model for the IDS prototype:\", SELECTED)\n", "print(\"rationale:\", rationale)\n", "print(\"-\" * 70)\n", "print(sel.loc[SELECTED].to_string())" ] }, { "cell_type": "markdown", "id": "a7635f80", "metadata": { "id": "a7635f80" }, "source": [ "## Final IDS Prototype\n", "\n", "The prototype is the deployment stage of CRISP-DM. It packages the preprocessing fitted during training together with the selected classifier so that a raw flow record in the UNSW-NB15 feature format is scored through exactly the transformations used at training time.\n", "\n", "The decision threshold is treated as an operating point rather than a fixed constant, because reducing false alarms is an explicit research objective and the default 0.5 cut-off is an arbitrary choice rather than an optimal one. Three candidate operating points are measured on the validation split: the default 0.5, the threshold that maximises validation F1, and the threshold that maximises detection subject to a one percent false positive budget. The F1-maximising point is deployed because it balances missed attacks against false alarms, which is what the research aim asks for; the constrained point is reported alongside it so that the trade-off available to an organisation with a stricter alert budget is visible. The testing partition plays no part in selecting the threshold.\n", "\n", "This is a research prototype demonstrating the detection approach, not a production enterprise IDS. It scores individual flow records that have already been extracted into UNSW-NB15 features; it does not capture packets, reassemble flows or block traffic." ] }, { "cell_type": "code", "execution_count": null, "id": "16b3ed1d", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:45.490526Z", "iopub.status.busy": "2026-09-08T07:16:45.490311Z", "iopub.status.idle": "2026-09-08T07:16:46.355021Z", "shell.execute_reply": "2026-09-08T07:16:46.353837Z" }, "id": "16b3ed1d", "outputId": "c0a20dd2-8e1f-4e16-e2e4-6de56554b5ff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "candidate operating points measured on the validation split:\n", " operating point threshold recall precision f1 fpr false_alarms missed_attacks\n", " default (0.5) 0.5000 0.9510 0.8964 0.9229 0.1046 1086 484\n", "max validation F1 0.4893 0.9560 0.8939 0.9239 0.1080 1121 435\n", " FPR <= 1% 0.8842 0.7254 0.9858 0.8358 0.0099 103 2712\n", "\n", "deployed threshold: 0.4893 (maximises validation F1)\n" ] } ], "source": [ "def score_matrix(model, X):\n", " if hasattr(model, \"predict_proba\"):\n", " return model.predict_proba(X)[:, 1]\n", " if isinstance(model, keras.Model):\n", " return model.predict(X, verbose=0).ravel()\n", " d = model.decision_function(X)\n", " return 1.0 / (1.0 + np.exp(-d))\n", "\n", "\n", "val_scores = score_matrix(selected_model, X_val)\n", "FPR_BUDGET = 0.01\n", "\n", "\n", "def op_stats(y_true, s, t):\n", " p = (s >= t).astype(int)\n", " tn, fp, fn, tp = confusion_matrix(y_true, p, labels=[0, 1]).ravel()\n", " return {\"threshold\": round(float(t), 4),\n", " \"recall\": tp / (tp + fn) if (tp + fn) else 0.0,\n", " \"precision\": tp / (tp + fp) if (tp + fp) else 0.0,\n", " \"f1\": f1_score(y_true, p, zero_division=0),\n", " \"fpr\": fp / (fp + tn) if (fp + tn) else 0.0,\n", " \"false_alarms\": int(fp), \"missed_attacks\": int(fn)}\n", "\n", "\n", "prec_v, rec_v, thr_pr = precision_recall_curve(y_val, val_scores)\n", "f1_curve = 2 * prec_v * rec_v / np.clip(prec_v + rec_v, 1e-12, None)\n", "t_f1 = float(thr_pr[int(np.argmax(f1_curve[:-1]))])\n", "\n", "fpr_v, tpr_v, thr_v = roc_curve(y_val, val_scores)\n", "allowed = fpr_v <= FPR_BUDGET\n", "t_budget = None\n", "if allowed.any():\n", " cand = float(thr_v[int(np.argmax(tpr_v * allowed))])\n", " t_budget = cand if np.isfinite(cand) else None\n", "\n", "candidates = {\"default (0.5)\": 0.5, \"max validation F1\": t_f1}\n", "if t_budget is not None:\n", " candidates[f\"FPR <= {FPR_BUDGET:.0%}\"] = t_budget\n", "\n", "op_table = pd.DataFrame([{\"operating point\": k, **op_stats(y_val, val_scores, t)}\n", " for k, t in candidates.items()])\n", "print(\"candidate operating points measured on the validation split:\")\n", "print(op_table.round(4).to_string(index=False))\n", "\n", "THRESHOLD = t_f1\n", "print(f\"\\ndeployed threshold: {THRESHOLD:.4f} (maximises validation F1)\")" ] }, { "cell_type": "code", "execution_count": null, "id": "4c512226", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:46.357962Z", "iopub.status.busy": "2026-09-08T07:16:46.357718Z", "iopub.status.idle": "2026-09-08T07:16:46.366415Z", "shell.execute_reply": "2026-09-08T07:16:46.365027Z" }, "id": "4c512226", "outputId": "26942a5c-0a08-4859-aafa-1c7be9a8a48d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IDS prototype ready | model: Artificial Neural Network | threshold: 0.4893 | features expected: 42\n" ] } ], "source": [ "class IntrusionDetector:\n", " \"\"\"Research prototype: preprocessing + selected classifier + operating threshold.\"\"\"\n", "\n", " def __init__(self, preprocessor, model, feature_cols, threshold=0.5, name=\"\"):\n", " self.preprocessor = preprocessor\n", " self.model = model\n", " self.feature_cols = list(feature_cols)\n", " self.threshold = float(threshold)\n", " self.name = name\n", "\n", " def _prepare(self, records):\n", " if isinstance(records, dict):\n", " records = [records]\n", " df = pd.DataFrame(records) if not isinstance(records, pd.DataFrame) else records.copy()\n", " missing = [c for c in self.feature_cols if c not in df.columns]\n", " if missing:\n", " raise ValueError(f\"missing required features: {missing[:8]}\")\n", " return self.preprocessor.transform(df[self.feature_cols])\n", "\n", " def predict(self, records):\n", " X = self._prepare(records).astype(np.float32)\n", " conf = score_matrix(self.model, X)\n", " flag = (conf >= self.threshold).astype(int)\n", " return pd.DataFrame({\n", " \"prediction\": np.where(flag == 1, \"MALICIOUS\", \"NORMAL\"),\n", " \"malicious_probability\": np.round(conf, 4),\n", " \"confidence\": np.round(np.where(flag == 1, conf, 1 - conf), 4),\n", " \"alert\": flag.astype(bool),\n", " })\n", "\n", "\n", "ids = IntrusionDetector(preprocessor, selected_model, FEATURES,\n", " threshold=THRESHOLD, name=SELECTED)\n", "print(\"IDS prototype ready | model:\", ids.name, \"| threshold:\", round(ids.threshold, 4),\n", " \"| features expected:\", len(ids.feature_cols))" ] }, { "cell_type": "markdown", "id": "3234df65", "metadata": { "id": "3234df65" }, "source": [ "### Prototype Demonstration on Real Testing Records" ] }, { "cell_type": "code", "execution_count": null, "id": "e44dd9b2", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:46.368603Z", "iopub.status.busy": "2026-09-08T07:16:46.368397Z", "iopub.status.idle": "2026-09-08T07:16:46.460385Z", "shell.execute_reply": "2026-09-08T07:16:46.458952Z" }, "id": "e44dd9b2", "outputId": "a00e88a6-d9b5-4ab3-cf2b-79df56658ab5" }, "outputs": [ { "data": { "text/html": [ "
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test_rowactualattack_catpredictionmalicious_probabilityconfidencealertcorrect
080322NORMALNormalMALICIOUS0.52700.5270TrueFalse
181681NORMALNormalNORMAL0.00020.9998FalseTrue
274087NORMALNormalMALICIOUS0.74280.7428TrueFalse
367175NORMALNormalMALICIOUS0.76820.7682TrueFalse
432651NORMALNormalNORMAL0.00020.9998FalseTrue
545233MALICIOUSFuzzersMALICIOUS0.86550.8655TrueTrue
64080MALICIOUSExploitsMALICIOUS1.00001.0000TrueTrue
746815MALICIOUSGenericMALICIOUS0.99240.9924TrueTrue
820856MALICIOUSGenericMALICIOUS1.00001.0000TrueTrue
91191MALICIOUSExploitsMALICIOUS0.91760.9176TrueTrue
\n", "
" ], "text/plain": [ " test_row actual attack_cat prediction malicious_probability confidence alert correct\n", "0 80322 NORMAL Normal MALICIOUS 0.5270 0.5270 True False\n", "1 81681 NORMAL Normal NORMAL 0.0002 0.9998 False True\n", "2 74087 NORMAL Normal MALICIOUS 0.7428 0.7428 True False\n", "3 67175 NORMAL Normal MALICIOUS 0.7682 0.7682 True False\n", "4 32651 NORMAL Normal NORMAL 0.0002 0.9998 False True\n", "5 45233 MALICIOUS Fuzzers MALICIOUS 0.8655 0.8655 True True\n", "6 4080 MALICIOUS Exploits MALICIOUS 1.0000 1.0000 True True\n", "7 46815 MALICIOUS Generic MALICIOUS 0.9924 0.9924 True True\n", "8 20856 MALICIOUS Generic MALICIOUS 1.0000 1.0000 True True\n", "9 1191 MALICIOUS Exploits MALICIOUS 0.9176 0.9176 True True" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng = np.random.RandomState(RANDOM_STATE)\n", "norm_idx = rng.choice(np.where(y_test == 0)[0], 5, replace=False)\n", "mal_idx = rng.choice(np.where(y_test == 1)[0], 5, replace=False)\n", "demo_idx = np.concatenate([norm_idx, mal_idx])\n", "\n", "demo_records = test_raw.iloc[demo_idx]\n", "out = ids.predict(demo_records[FEATURES])\n", "out.insert(0, \"actual\", np.where(y_test[demo_idx] == 1, \"MALICIOUS\", \"NORMAL\"))\n", "out.insert(1, \"attack_cat\", demo_records[\"attack_cat\"].to_numpy())\n", "out.insert(0, \"test_row\", demo_idx)\n", "out[\"correct\"] = out[\"actual\"] == out[\"prediction\"]\n", "out" ] }, { "cell_type": "code", "execution_count": null, "id": "ce433064", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:46.462629Z", "iopub.status.busy": "2026-09-08T07:16:46.462403Z", "iopub.status.idle": "2026-09-08T07:16:46.537428Z", "shell.execute_reply": "2026-09-08T07:16:46.536395Z" }, "id": "ce433064", "outputId": "c3566522-5ee3-4f49-91d5-0402f3ebf14c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "single flow record submitted to the prototype:\n", "{'dur': 0.354678, 'proto': 'tcp', 'service': '-', 'state': 'FIN', 'spkts': 10, 'dpkts': 6, 'sbytes': 2588, 'dbytes': 268, 'rate': 42.291881, 'sttl': 254} ...\n", "\n", "prediction malicious_probability confidence alert\n", " MALICIOUS 0.9176 0.9176 True\n" ] } ], "source": [ "single = test_raw.iloc[int(demo_idx[-1])][FEATURES].to_dict()\n", "print(\"single flow record submitted to the prototype:\")\n", "print({k: single[k] for k in list(single)[:10]}, \"...\")\n", "print()\n", "print(ids.predict(single).to_string(index=False))" ] }, { "cell_type": "code", "execution_count": null, "id": "8f9c7378", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:46.540585Z", "iopub.status.busy": "2026-09-08T07:16:46.540366Z", "iopub.status.idle": "2026-09-08T07:16:50.886021Z", "shell.execute_reply": "2026-09-08T07:16:50.885509Z" }, "id": "8f9c7378", "outputId": "6fbafcf9-ed62-47dd-edff-1de56d2c4eb9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Artificial Neural Network @ threshold 0.4893\n", " accuracy 0.8591 precision 0.8027 recall 0.9865 f1 0.8852\n", " TPR 0.9865 FPR 0.2971 FNR 0.0135 AUC 0.9827\n", " TN 26006 FP 10994 FN 610 TP 44722\n", "\n", "end-to-end throughput including preprocessing: 19,311 flow records/second (4.3s for 82,332 records)\n", "\n", " operating point recall precision f1 fpr false alarms missed attacks\n", " default threshold 0.5 0.9856 0.8071 0.8875 0.2885 10676 655\n", "tuned threshold 0.4893 0.9865 0.8027 0.8852 0.2971 10994 610\n" ] } ], "source": [ "t0 = time.time()\n", "proto_out = ids.predict(test_raw[FEATURES])\n", "elapsed = time.time() - t0\n", "proto_pred = proto_out[\"alert\"].to_numpy().astype(int)\n", "\n", "deployed = evaluate(y_test, proto_pred, scores[SELECTED], f\"{SELECTED} @ threshold {ids.threshold:.4f}\")\n", "show(deployed)\n", "print()\n", "print(f\"end-to-end throughput including preprocessing: {len(y_test)/elapsed:,.0f} flow records/second \"\n", " f\"({elapsed:.1f}s for {len(y_test):,} records)\")\n", "\n", "default_row = results[SELECTED]\n", "print()\n", "print(pd.DataFrame([\n", " {\"operating point\": \"default threshold 0.5\", **{k: round(default_row[k], 4)\n", " for k in (\"recall\", \"precision\", \"f1\", \"fpr\")}, \"false alarms\": default_row[\"fp\"],\n", " \"missed attacks\": default_row[\"fn\"]},\n", " {\"operating point\": f\"tuned threshold {ids.threshold:.4f}\", **{k: round(deployed[k], 4)\n", " for k in (\"recall\", \"precision\", \"f1\", \"fpr\")}, \"false alarms\": deployed[\"fp\"],\n", " \"missed attacks\": deployed[\"fn\"]},\n", "]).to_string(index=False))" ] }, { "cell_type": "markdown", "id": "d519456f", "metadata": { "id": "d519456f" }, "source": [ "### Persisting the Artefact" ] }, { "cell_type": "code", "execution_count": null, "id": "7021bd3b", "metadata": { "execution": { "iopub.execute_input": "2026-09-08T07:16:50.888952Z", "iopub.status.busy": "2026-09-08T07:16:50.888592Z", "iopub.status.idle": "2026-09-08T07:16:50.958047Z", "shell.execute_reply": "2026-09-08T07:16:50.956136Z" }, "id": "7021bd3b", "outputId": "bd85f6da-45d2-4d5a-fd59-5f1970e733e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['config.json', 'model.keras', 'model_comparison.csv', 'preprocessor.joblib']\n" ] } ], "source": [ "import joblib\n", "os.makedirs(\"ids_artifact\", exist_ok=True)\n", "joblib.dump(preprocessor, \"ids_artifact/preprocessor.joblib\")\n", "if isinstance(selected_model, keras.Model):\n", " selected_model.save(\"ids_artifact/model.keras\")\n", " model_file = \"ids_artifact/model.keras\"\n", "else:\n", " joblib.dump(selected_model, \"ids_artifact/model.joblib\")\n", " model_file = \"ids_artifact/model.joblib\"\n", "\n", "with open(\"ids_artifact/config.json\", \"w\") as fh:\n", " json.dump({\"selected_model\": SELECTED, \"threshold\": ids.threshold,\n", " \"features\": FEATURES, \"categorical\": CAT_COLS,\n", " \"random_state\": RANDOM_STATE, \"model_file\": model_file}, fh, indent=2)\n", "\n", "comparison.to_csv(\"ids_artifact/model_comparison.csv\")\n", "print(sorted(os.listdir(\"ids_artifact\")))" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.15" } }, "nbformat": 4, "nbformat_minor": 5 }