Supporting CAN Bus Anomaly Detection with Correlation Data

Beatrix Koltai, András Gazdag, Gergely Ács

2024

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 285-296. DOI: 10.5220/0012360400003648

in Bibtex Style

@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 - Volume 1: ICISSP},
year={2024},
pages={285-296},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012360400003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Supporting CAN Bus Anomaly Detection with Correlation Data
SN - 978-989-758-683-5
AU - Koltai B.
AU - Gazdag A.
AU - Ács G.
PY - 2024
SP - 285
EP - 296
DO - 10.5220/0012360400003648
PB - SciTePress