| Title | TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Thiruloga, Sooryaa Vignesh, Kukkala, Vipin Kumar, Pasricha, Sudeep |
| Conference Name | 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC) |
| Keywords | composability, compositionality, convolution, correlation coefficient, Cyber Dependencies, Cyber-physical systems, design automation, Human Behavior, human factors, Measurement, Metrics, Neural networks, pubcrawl, resilience, Resiliency, Roads, Scalability |
| 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 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 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, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection. |
| DOI | 10.1109/ASP-DAC52403.2022.9712524 |
| Citation Key | thiruloga_tenet_2022 |
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems