IntelliTamper: Preventing Physical Attacks with Intelligent Sensor Fusion

IntelliTamper: Preventing Physical Attacks with Intelligent Sensor Fusion

IntelliTamper is a tamper-detection solution that combines multiple sensor inputs with intelligent processing to detect, classify, and respond to physical attacks on devices and equipment. It’s designed for environments where device integrity matters: IoT endpoints, industrial controllers, payment terminals, ATMs, medical devices, and critical infrastructure.

Key components

• Multimodal sensors: accelerometers, gyroscopes, magnetometers, light sensors, temperature sensors, pressure sensors, microphones, and intrusion switches.
• Sensor fusion engine: combines raw signals to create robust features that reduce false positives from single-sensor anomalies.
• Edge ML models: lightweight classifiers running on-device to detect patterns indicative of tampering (prying, drilling, opening, relocation, thermal attacks).
• Event manager: prioritizes alerts, logs incidents, and triggers local mitigations (lockdown, wipe, disable interfaces) or secure notifications to backend systems.
• Secure telemetry: cryptographically signed event reports with tamper-evident logging to support forensics.

How it works (workflow)

1. Continuous sensing and feature extraction on-device.
2. Fusion of sensor streams to form composite indicators (e.g., simultaneous vibration + magnetic disturbance).
3. On-device inference classifies events as benign (e.g., normal handling) or malicious.
4. If malicious, the event manager executes configured responses and sends encrypted alerts to monitoring services.
5. Secure audit trails assist in incident investigation and compliance.

Benefits

• Higher accuracy: sensor fusion reduces false alarms vs. single-sensor approaches.
• Low latency: on-device detection enables immediate mitigations without cloud round-trip.
• Privacy-preserving: most processing occurs locally; only essential, signed alerts are transmitted.
• Forensic value: tamper-evident logs and correlated sensor data help reconstruct attacks.
• Flexible deployment: configurable sensitivity and response profiles per device class.

Typical use cases

• ATMs and payment terminals — detect skimming, drilling, or unauthorized opening.
• Industrial control systems — detect panel breaches or relocations.
• Medical devices — protect against physical interference or unauthorized access.
• High-value retail displays — detect theft attempts or tampering.
• Remote sensors — detect tampering or unauthorized redeployment.

Implementation considerations

• Calibrate sensor thresholds per device and environment to balance sensitivity vs. false positives.
• Use hardware-backed keys and secure boot to protect ML models and event integrity.
• Provide remote update mechanisms for model and rule improvements.
• Design fail-safe behaviors that preserve safety and data integrity under false positives.
• Ensure encrypted, authenticated telemetry and retention policies for incident logs.

Example alert types

• Soft tamper: sudden orientation changes + moderate vibration — alert level: medium.
• Hard tamper: drilling noise spectrum + prolonged vibration + magnetic field disruption — alert level: critical, initiate lockdown.
• Environmental spoofing: abrupt light/temperature changes without expected operating context — alert level: investigate.