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Multiple attacks have shown that in-vehicle networks have vulnerabilitieswhich can be exploited. Securing the Controller Area Network (CAN) for modernvehicles has become a necessary task for car manufacturers. Some attacks injectpotentially large amount of fake messages into the CAN network; however, suchattacks are relatively easy to detect. In more sophisticated attacks, theoriginal messages are modified, making the detection a more complex problem. Inthis paper, we present a novel machine learning based intrusion detectionmethod for CAN networks. We focus on detecting message modification attacks,which do not change the timing patterns of communications. Our proposedtemporal convolutional network-based solution can learn the normal behavior ofCAN signals and differentiate them from malicious ones. The method is evaluatedon multiple CAN-bus message IDs from two public datasets including differenttypes of attacks. Performance results show that our lightweight approachcompares favorably to the state-of-the-art unsupervised learning approach,achieving similar or better accuracy for a wide range of scenarios with asignificantly lower false positive rate.

Detecting message modification attacks on the CAN bus with Temporal Convolutional Networks