Exploring inherent sensor redundancy for automotive anomaly detection

The increasing autonomy and connectivity have been transitioning automobiles to complex and open architectures that are vulnerable to malicious attacks beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This concern emphasizes the need to validate sensor data before acting on them. Unlike existing works, this paper exploits inherent redundancy among heterogeneous sensors for detecting anomalous sensor measurements. The redundancy is that multiple sensors simultaneously respond to the same physical phenomenon in a related fashion. Embedding the redundancy into a deep autoencoder, we propose an anomaly detector that learns a consistent pattern from vehicle sensor data in normal states and utilizes it as the nominal behavior for the detection. The proposed method is independent of the scarcity of anomalous data for training and the intensive calculation of pairwise correlation among senors as in existing works. Using a real-world data set collected from tens of vehicle sensors, we demonstrate the feasibility and efficacy of the proposed method.