TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems
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 cybe...
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| Published in: | Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference pp. 326 - 331 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
17.01.2022
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| Subjects: | |
| ISSN: | 2153-697X |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 2153-697X |
| DOI: | 10.1109/ASP-DAC52403.2022.9712524 |