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Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus.

Kalutarage, Harsha Kumara; Al-Kadri, M. Omar; Cheah, Madeline; Madzudzo, Garikayi


Harsha Kumara Kalutarage

M. Omar Al-Kadri

Madeline Cheah

Garikayi Madzudzo


Hans-Joachim Hof

Mario Fritz

Christoph Kraub

Oliver Wasenmüller


Automotive electronics is rapidly expanding. An average vehicle contains million lines of software codes, running on 100 of electronic control units (ECUs), in supporting number of safety, driver assistance and infotainment functions. These ECUs are networked using a Controller Area Network (CAN). Security of the CAN bus has not historically been a major concern, however, recent research demonstrate that CAN has many vulnerabilities to cyber attacks. This paper presents a contextualised anomaly detector for monitoring cyber attacks on the CAN bus. Proposed algorithm is based on message sequence modelling, using so called N-grams distributions. It utilises only benign data (one class) for training and threshold estimation. Performance of the algorithm was tested against two different attack scenarios, RPM and gear gauge messages spoofing, using data captured from a real vehicle. Experimental outcomes demonstrate that proposed algorithm is capable of detecting both attacks with 100% accuracy, using far smaller time windows (100ms) which is essential for a practically deployable automotive cyber security solution.

Start Date Oct 8, 2019
Publication Date Oct 8, 2019
Publisher Association for Computing Machinery
ISBN 9781450370042
Institution Citation KALUTARAGE, H.K., AL-KADRI, M.O., CHEAH, M. and MADZUDZO, G. 2019. Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. In Hof, H.-J., Fritz, M., Kraub, C. and Wasenm├╝ller, O. (eds.). Proceedings of 2019 Computer science in cars symposium (CSCS 2019), 8 October 2019, Kaiserslautern, Germany. New York: ACM [online], article number 7. Available from:
Keywords In-vehicle networks; CAN bus; Automotive cyber security; Context-aware anomaly detection


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