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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference s. 326 - 331
Hlavní autori: Thiruloga, Sooryaa Vignesh, Kukkala, Vipin Kumar, Pasricha, Sudeep
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 17.01.2022
Predmet:
Convolution
Correlation coefficient
Cyber-physical systems
Design automation
Measurement
Neural networks
Roads
ISSN:2153-697X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
ISSN:2153-697X
DOI:10.1109/ASP-DAC52403.2022.9712524