TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems
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Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to detect anomalous attack patterns. TENET is able to achieve an improvement of 32.70 Coefficient, and 17.25 parameters, 86.95 time when compared to the best performing prior work on automotive anomaly detection.
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