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