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.
@article{arxiv.2109.04565,
title = {TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems},
author = {S. V. Thiruloga and V. K. Kukkala and S. Pasricha},
journal= {arXiv preprint arXiv:2109.04565},
year = {2021}
}