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Authors: Beatrix Koltai ; András Gazdag and Gergely Ács

Affiliation: Laboratory of Cryptography and System Security (CrySyS Lab), Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary

Keyword(s): CAN, Anomaly Detection, TCN, Correlation.

Abstract: Communication on the Controller Area Network (CAN) in vehicles is notably lacking in security measures, rendering it susceptible to remote attacks. These cyberattacks can potentially compromise safety-critical vehicle subsystems, and therefore endanger passengers and others around them. Identifying these intrusions could be done by monitoring the CAN traffic and detecting abnormalities in sensor measurements. To achieve this, we propose integrating time-series forecasting and signal correlation analysis to improve the detection accuracy of an onboard intrusion detection system (IDS). We predict sets of correlated signals collectively and report anomaly if their combined prediction error surpasses a predefined threshold. We show that this integrated approach enables the identification of a broader spectrum of attacks and significantly outperforms existing state-of-the-art solutions.

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Paper citation in several formats:
Koltai, B.; Gazdag, A. and Ács, G. (2024). Supporting CAN Bus Anomaly Detection with Correlation Data. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-683-5; ISSN 2184-4356, SciTePress, pages 285-296. DOI: 10.5220/0012360400003648

@conference{icissp24,
author={Beatrix Koltai. and András Gazdag. and Gergely Ács.},
title={Supporting CAN Bus Anomaly Detection with Correlation Data},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - ICISSP},
year={2024},
pages={285-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012360400003648},
isbn={978-989-758-683-5},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - ICISSP
TI - Supporting CAN Bus Anomaly Detection with Correlation Data
SN - 978-989-758-683-5
IS - 2184-4356
AU - Koltai, B.
AU - Gazdag, A.
AU - Ács, G.
PY - 2024
SP - 285
EP - 296
DO - 10.5220/0012360400003648
PB - SciTePress