Implement reliable tracker detection for AirTag, Tile, Samsung SmartTag,
and other BLE trackers based on manufacturer data patterns, service UUIDs,
and advertising payload analysis.
Key changes:
- Add TrackerSignatureEngine with signatures for major tracker brands
- Device fingerprinting to track devices across MAC randomization
- Suspicious presence heuristics (persistence, following patterns)
- New API endpoints: /api/bluetooth/trackers, /diagnostics
- UI updates with tracker badges, confidence, and evidence display
- TSCM integration updated to use v2 tracker detection data
- Unit tests and smoke test scripts for validation
Detection is heuristic-based with confidence scoring (high/medium/low)
and evidence transparency. Backwards compatible with existing APIs.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Introduces standardized RSSI-to-label mapping (minimal/weak/moderate/strong/very_strong)
and duration-based confidence modifiers for client-facing reports and dashboards.
- New signal_classification.py module with hedged language generation
- Updated detector.py to use standardized signal descriptions
- Enhanced reports.py with signal classification in findings
- Added JS SignalClassification and signal indicator components
- CSS styles for signal strength bars and assessment panels
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Classification levels:
- Green (Informational): Known devices in baseline, expected infrastructure
- Yellow (Needs Review): Unknown BLE devices, new WiFi APs, unidentified RF
- Red (High Interest): Persistent transmitters, audio-capable BLE, trackers,
devices with repeat detections across scans
Features:
- Device history tracking for repeat detection (24-hour window)
- Audio-capable BLE detection (headphones, mics, speakers)
- Classification reasons shown under each device
- Color-coded indicators with visual styling
- Microphone badge for audio-capable BLE devices
