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On the Resilience of Machine Learning-Based IDS for Automotive Networks
RISE Research Institutes of Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-2772-4661
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-6116-164X
RISE Research Institutes of Sweden.
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2023 (English)In: proc of IEEE Vehicular Networking Conference, VNC, IEEE Computer Society , 2023, p. 239-246Conference paper, Published paper (Refereed)
Abstract [en]

Modern automotive functions are controlled by a large number of small computers called electronic control units (ECUs). These functions span from safety-critical autonomous driving to comfort and infotainment. ECUs communicate with one another over multiple internal networks using different technologies. Some, such as Controller Area Network (CAN), are very simple and provide minimal or no security services. Machine learning techniques can be used to detect anomalous activities in such networks. However, it is necessary that these machine learning techniques are not prone to adversarial attacks. In this paper, we investigate adversarial sample vulnerabilities in four different machine learning-based intrusion detection systems for automotive networks. We show that adversarial samples negatively impact three of the four studied solutions. Furthermore, we analyze transferability of adversarial samples between different systems. We also investigate detection performance and the attack success rate after using adversarial samples in the training. After analyzing these results, we discuss whether current solutions are mature enough for a use in modern vehicles.

Place, publisher, year, edition, pages
IEEE Computer Society , 2023. p. 239-246
Keywords [en]
Adversarial AI/ML, Controller Area Network, Intrusion Detection System, Machine Learning, Vehicle Security, Computer crime, Control system synthesis, Controllers, Intrusion detection, Learning algorithms, Network security, Process control, Safety engineering, Automotive networks, Automotives, Autonomous driving, Controller-area network, Electronics control unit, Intrusion Detection Systems, Machine learning techniques, Machine-learning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:ri:diva-65727DOI: 10.1109/VNC57357.2023.10136285Scopus ID: 2-s2.0-85163164299ISBN: 9798350335491 (electronic)OAI: oai:DiVA.org:ri-65727DiVA, id: diva2:1787089
Conference
14th IEEE Vehicular Networking Conference, VNC 2023.Instanbul. 26 April 2023 through 28 April 2023.
Note

This research is partially funded by the CyReV project(Sweden’s Innovation Agency, D-nr 2019-03071), partiallyby the H2020 ARCADIAN-IoT (Grant ID. 101020259), andH2020 VEDLIoT (Grant ID. 957197).

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2024-03-03Bibliographically approved

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