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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.ORCID iD: 0000-0002-2772-4661
RISE Research Institutes of Sweden.ORCID iD: 0000-0001-6116-164X
RISE Research Institutes of Sweden.
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2023 (English)In: 2023 IEEE Vehicular Networking Conference (VNC), Institute of Electrical and Electronics Engineers (IEEE), 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
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 239-246
Series
IEEE Vehicular Networking Conference, ISSN 2157-9857, E-ISSN 2157-9865
Keywords [en]
Vehicle Security, Machine Learning, Controller Area Network, Intrusion Detection System, Adversarial AI/ML
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-511291DOI: 10.1109/VNC57357.2023.10136285ISI: 001011821500047ISBN: 979-8-3503-3549-1 (electronic)ISBN: 979-8-3503-3550-7 (print)OAI: oai:DiVA.org:uu-511291DiVA, id: diva2:1796191
Conference
2023 IEEE Vehicular Networking Conference (VNC), 26-28 April, Istanbul, Turkiye
Funder
Vinnova, 2019-03071EU, Horizon 2020, 101020259EU, Horizon 2020, 957197Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-15Bibliographically approved
In thesis
1. Robust and Efficient Federated Learning for IoT Security
Open this publication in new window or tab >>Robust and Efficient Federated Learning for IoT Security
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The widespread adoption of Internet of Things (IoT) devices has led to substantial progress across various industrial sectors, including healthcare, transportation, and manufacturing. However, these devices also introduce significant security vulnerabilities because they are often deployed without adequate security measures, making them susceptible to cyber threats. Meanwhile, the rapid evolution of Artificial Intelligence (AI), specifically in the fields of Machine Learning (ML)  and Deep Learning (DL),  brings convenience and advantages to the community of IoT security. AI-driven solutions can process extensive data from IoT devices and networks, facilitating the identification of intricate and dynamic threats that may go unnoticed through conventional security methods. Nevertheless, typical ML models require a substantial volume of centralized datasets for training, which may conflict with the principles outlined in the GDPR. Recently, Federated Learning (FL) has emerged as a promising decentralized learning paradigm that enables participants to collaboratively train models without sharing private data. However, FL also brings new challenges.

The contributions of this dissertation are presented through six research papers, which address identified shortcomings and challenges of FL and ML. Initially, a comprehensive landscape study is conducted to understand available ML technologies thoroughly. A novel approach to device fingerprinting and identification is proposed to fingerprint and identify IoT devices through the application of FL. Through this work, several limitations of FL and research challenges are identified. To begin with, the challenges of non-IID and imbalanced data are addressed by proposing adaptive data rebalancing techniques in a peer-to-peer FL setup. Subsequently, a communication-efficient and robust federated aggregation rule is proposed to secure the learning process in the FL setup. Furthermore, when the Intrusion Detection System (IDS) detects anomaly records, they are shared as vulnerability alerts with the Cyber Threat Intelligence platform, which is enhanced by the proposed ML-based functionalities to automate threat processing. Lastly, an in-vehicle IDS is analyzed in the context of the automotive use case for its resilience against adversarial attacks.

The overall contribution of this dissertation enhances the aggregation methodology within FL, emphasizes its adaptability in addressing diverse critical scenarios to tackle IoT security challenges, and reinforces ML models to confront adversarial AI challenges. Given that FL is still in its early stages, with numerous unresolved challenges in IoT security, these enhancements and contributions are timely in paving the way for future advancements and providing a clearer path forward.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 59
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2306
Keywords
Internet of Things, Federated Learning, Machine Learning, Intrusion Detection System, Communication Efficiency, Robustness, Adversarial AI, Device Fingerprinting, Device Identification, Cyber Threat Intelligence
National Category
Computer Systems
Research subject
Computer Science with specialization in Computer Communication
Identifiers
urn:nbn:se:uu:diva-511774 (URN)978-91-513-1895-0 (ISBN)
Public defence
2023-11-02, 80127, Ångström, Lägerhyddsvägen 1, Uppsala, 13:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 101020259EU, Horizon 2020, 830927
Available from: 2023-10-11 Created: 2023-09-15 Last updated: 2023-10-11

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