Drone API Security Operations
Security Overview
Threat intelligence workspace
See every request.
Reconstruct every incident.
Operational drone data, enforced GraphQL controls, anomaly detection and forensic evidence—connected in one local assessment environment.
Live posture
Security control status
HMAC AuthenticationSigned bearer tokensENFORCED
Role-Based AccessFour permission tiersENFORCED
AST Query ControlsFragments, depth, cost & batchingENFORCED
Evidence Hash ChainModification and sequence verificationCHECK
Session
Sign in with a seeded role to load live fleet metrics and security evidence.
Operational baseline
Fleet, missions & telemetry
Manage simulated assets and generate legitimate GraphQL activity for comparison.
Asset registry
Drones
| ID | Serial | Model | Status | Battery | Location |
|---|
Create asset
Add demo drone
Mission control
Mission records
| ID | Title | Type | Status | Drone | Target |
|---|
Create tasking
New mission
Flight sensor stream
Latest telemetry
| Time (UTC) | Drone | Latitude | Longitude | Altitude | Speed | Battery |
|---|
Controlled environment
GraphQL Attack Laboratory
Generate repeatable, labelled evidence safely inside the local database. No real attack traffic is sent.
Ethical boundaryThese controls insert simulated security events for academic evaluation. They do not probe real drones, networks or third-party systems.
Broken Access Control
Simulates unauthorised access to protected mission data.
Deep Query Abuse
Models nested-query resource exhaustion behaviour.
Introspection Probe
Captures schema-enumeration indicators and fields.
Telemetry Scraping
Simulates high-volume extraction of drone coordinates.
Mission Tampering
Models a viewer attempting a privileged mutation.
Failed Login Burst
Creates repeated authentication-failure evidence.
Simulation output
Laboratory activity
- Choose a scenario to begin the controlled assessment.
Detection engineering
AI & machine-learning analysis
Compare transparent security rules with supervised classifiers and unsupervised anomaly detection.
Supervised
Decision Tree
Interpretable classification paths.
Recommended
Random Forest
Balanced ensemble classification.
Interpretable baseline
Logistic Regression
Calibrated linear probability model.
Randomised ensemble
Extra Trees
High-variance attack-pattern comparison.
Unsupervised
Isolation Forest
Novel-pattern anomaly discovery.
Evaluation
Model benchmark results
Run training to display accuracy, precision, recall, F1 and error rates.
Latest run
Awaiting analysis
—anomalies detectedSelect rule-based or ML detection.
Incident response
Forensic reconstruction
Correlate actors, operations, affected assets, outcomes and cryptographic evidence.
Chronology
Incident timeline
Build the report to reconstruct suspicious activity.
Forensic evidence store
GraphQL security logs
Search captured requests, verify the evidence chain and export examiner-ready artifacts.
| Timestamp | Actor / Role | Operation | Depth / Cost | Auth | Risk | Classification | Evidence hash |
|---|