Authors:
Irina Chiscop
1
;
András Gazdag
2
;
Joost Bosman
1
and
Gergely Biczók
2
Affiliations:
1
Cyber Security & Robustness Department, TNO, The Hague, The Netherlands
;
2
CrySyS Lab., Dept. of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary
Keyword(s):
Vehicle Security, Intrusion Detection, Controller Area Network, Machine Learning, Temporal Convolutional Networks.
Abstract:
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 co
mpares 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.
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