Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems

Published in Springer Nature, 2023

Recommended citation: S. V. Thiruloga, V. K. Kukkala, and S. Pasricha, "Deep AI for Anomaly Detection in Automotive Cyber-Physical Systems," in Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems, Springer Nature, 2023.

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 chapter, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks (CNNs) with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post-deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET achieves 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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