Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles
This addresses security vulnerabilities in autonomous vehicles that could endanger drivers, nearby vehicles, and pedestrians, but it appears incremental as it builds on existing anomaly detection methods.
The paper tackled the problem of detecting sensor attacks like spoofing and false data injection in autonomous vehicles, proposing a spatial-temporal anomaly detection model called STAnDS, which was shown to be effective in simulated tests.
Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted braking control. Despite their relative importance in making safer driving decisions, these devices are vulnerable to multiple attack types including spoofing, triggering and false data injection. When these attacks are successful they can compromise the security of autonomous vehicles leading to severe consequences for the driver, nearby vehicles and pedestrians. To handle these attacks and protect the measurement devices, we propose a spatial-temporal anomaly detection model \textit{STAnDS} which incorporates a residual error spatial detector, with a time-based expected change detection. This approach is evaluated using a simulated quantitative environment and the results show that \textit{STAnDS} is effective at detecting multiple attack types.