The Controller Area Network (CAN) bus has been a widely implemented standard for in-vehicle communication between vehicle subsystems. However, since CAN was never designed with a focus on security, attackers can exploit the lack of message authentication in CAN to inject crafted malicious payloads to disable critical systems onboard the vehicle. While previous works in literature focus on detecting deviations in the normal behavior of the bus, they merely focus on individual sensors. Hence they fail to identify stealthy attacks that do not cause individual sensors to deviate substantially from their expected behavior but still have a significant impact on the bus state. Further, such approaches often impose a computational strain on the deployed system due to the high magnitude of consumed resources at run-time. To this end, we propose CANLite, a lightweight anomaly detection system utilizing multitask learning to detect such subtle deviations while significantly reducing the memory footprint. We trained and evaluated our model against a state-of-the-art baseline approach. Our results indicate that CANLite reduces the memory footprint by 50% while still achieving the same level of detection performance as the baseline.
CANLite: Anomaly Detection in Controller Area Networks with Multitask Learning
2022-06-01
931450 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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