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arXiv · 2109.04565

TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems

S. V. Thiruloga·V. K. Kukkala·S. Pasricha
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Abstract

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% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, 86.95% decrease in memory footprint, and 48.14% lower inference time when compared to the best performing prior work on automotive anomaly detection.

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S. V. Thiruloga, V. K. Kukkala, S. Pasricha. 2021-09-09. TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems. https://arxiv.org/abs/2109.04565

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