  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.
