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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Fingerprinting ECUs to Implement Vehicular Security for Passenger Safety Using Machine Learning Techniques
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.
more »
« less
- Award ID(s):
- 2035770
- NSF-PAR ID:
- 10395373
- Date Published:
- Journal Name:
- Intelligence Systems (IntelliSys'22)
- ISSN:
- 1055-0542
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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