Abstract
The Controller Area Network (CAN) protocol used in vehicles today was designed to be fast, reliable, and robust. However, it is inherently insecure due to its lack of any kind of message authentication. Despite this, CAN is still used extensively in the automotive industry for various electronic control units (ECUs) and sensors which perform critical functions such as engine control. This paper presents a novel methodology for in-vehicle security through fingerprinting of ECUs. The proposed research uses the fingerprints injected in the signal due to material imperfections and semiconductor impurities. By extracting features from the physical CAN signal and using them as inputs for a machine learning algorithm, it is possible to determine the sender ECU of a packet. A high classification accuracy of up to 100.0% is possible when every node on the bus has a sufficiently different channel length.
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Acknowledgment
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for supporting this work through the project # DRI-KSU-934. This research is also partly supported by National Science Foundation (NSF) under the award # 2035770.
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Bellaire, S., Bayer, M., Hafeez, A., Refat, R.U.D., Malik, H. (2023). Fingerprinting ECUs to Implement Vehicular Security for Passenger Safety Using Machine Learning Techniques. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_2
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DOI: https://doi.org/10.1007/978-3-031-16075-2_2
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