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Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks

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Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

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Abstract

Since communication security is not a primary concern at the beginning of in-vehicle network protocol design (e.g., controller area network, CAN), it is not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN security. These IDS mechanisms rely on spatiotemporal information of CAN data frames. Given limited computational power of in-vehicle electronic control units, lightweight IDS is preferred.

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Acknowledgements

The author is supported by the National Natural Science Foundation of China (61971192), Shanghai Municipal Education Commission (2021-01-07-00-08-E00101), and Shanghai Trusted Industry Internet Software Collaborative Innovation Center.

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Authors and Affiliations

  1. East China Normal University and Shanghai Key Laboratory of Trustworthy Computing, Shanghai, China

    Xiangxue Li

  2. Shanghai Key Laboratory of Privacy-Preserving Computation, MatrixElements Technologies, Shanghai, China

    Xiangxue Li

  3. CATARC Software Testing (Tianjin) Co. Ltd, Tianjin, China

    Yue Bao & Xintian Hou

Authors
  1. Xiangxue Li
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  2. Yue Bao
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  3. Xintian Hou
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Correspondence to Xiangxue Li .

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Editors and Affiliations

  1. NVIDIA, Santa Clara, CA, USA

    Vipin Kumar Kukkala

  2. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

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Li, X., Bao, Y., Hou, X. (2023). Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks. In: Kukkala, V.K., Pasricha, S. (eds) Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-28016-0_14

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  • DOI: https://doi.org/10.1007/978-3-031-28016-0_14

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  • Online ISBN: 978-3-031-28016-0

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