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Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders

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Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

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Abstract

Modern vehicles consist of several powerful embedded systems called Electronic Control Units (ECUs), which control different subsystems in the vehicle. The increasing efforts to make vehicles fully autonomous have led to high reliance on information from various external sources, which made the ECUs in the vehicles highly vulnerable to various cyber-attacks. Therefore, it is essential to have a robust detection system in vehicles that can detect various cyber-attacks. To address this issue, in this chapter, we present a novel deep learning-based intrusion detection framework called INDRA that utilizes a Gated Recurrent Unit (GRU) based recurrent autoencoder network to detect various cyber-attacks in automotive cyber-physical systems. Moreover, the INDRA framework is evaluated under different attack scenarios and compared against various state-of-the-art intrusion detection works using a commercially available in-vehicle network dataset.

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Author information

Authors and Affiliations

  1. NVIDIA, Santa Clara, CA, USA

    Vipin Kumar Kukkala

  2. Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA

    Sooryaa Vignesh Thiruloga

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

    Sudeep Pasricha

Authors
  1. Vipin Kumar Kukkala
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  2. Sooryaa Vignesh Thiruloga
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  3. Sudeep Pasricha
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Corresponding author

Correspondence to Vipin Kumar Kukkala .

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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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Kukkala, V.K., Thiruloga, S.V., Pasricha, S. (2023). Real-Time Intrusion Detection in Automotive Cyber-Physical Systems with Recurrent Autoencoders. 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_10

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  • DOI: https://doi.org/10.1007/978-3-031-28016-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28015-3

  • Online ISBN: 978-3-031-28016-0

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