  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.
