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Connected and autonomous vehicles: A cyber-risk classification framework

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  • Sheehan, Barry
  • Murphy, Finbarr
  • Mullins, Martin
  • Ryan, Cian

Abstract

The proliferation of technologies embedded in connected and autonomous vehicles (CAVs) increases the potential of cyber-attacks. The communication systems between vehicles and infrastructure present remote attack access for malicious hackers to exploit system vulnerabilities. Increased connectivity combined with autonomous driving functions pose a considerable threat to the vast socioeconomic benefits promised by CAVs. However, the absence of historical information on cyber-attacks mean that traditional risk assessment methods are rendered ineffective. This paper proposes a proactive CAV cyber-risk classification model which overcomes this issue by incorporating known software vulnerabilities contained within the US National Vulnerability Database into model building and testing phases. This method uses a Bayesian Network (BN) model, premised on the variables and causal relationships derived from the Common Vulnerability Scoring Scheme (CVSS), to represent the probabilistic structure and parameterisation of CAV cyber-risk. The resulting BN model is validated with an out-of-sample test demonstrating nearly 100% prediction accuracy of the quantitative risk score and qualitative risk level. The model is then applied to the use-case of GPS systems of a CAV with and without cryptographic authentication. In the use case, we demonstrate how the model can be used to predict the effect of risk reduction measures.

Suggested Citation

  • Sheehan, Barry & Murphy, Finbarr & Mullins, Martin & Ryan, Cian, 2019. "Connected and autonomous vehicles: A cyber-risk classification framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 523-536.
  • Handle: RePEc:eee:transa:v:124:y:2019:i:c:p:523-536
    DOI: 10.1016/j.tra.2018.06.033
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    References listed on IDEAS

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    1. Vinayak V Dixit & Sai Chand & Divya J Nair, 2016. "Autonomous Vehicles: Disengagements, Accidents and Reaction Times," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-14, December.
    2. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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    Cited by:

    1. Ding, Rui & Liu, Zehua & Xu, Jintao & Meng, Fanpeng & Sui, Yang & Men, Xinhong, 2021. "A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.
    3. Rahim, Muddasir & Javed, Muhammad Awais & Alvi, Ahmad Naseem & Imran, Muhammad, 2020. "An efficient caching policy for content retrieval in autonomous connected vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 142-152.
    4. Sallam, Gamal & Baroudi, Uthman, 2020. "A two-stage framework for fair autonomous robot deployment using virtual forces," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 35-50.
    5. Jannusch, Tim & David-Spickermann, Florian & Shannon, Darren & Ressel, Juliane & Völler, Michaele & Murphy, Finbarr & Furxhi, Irini & Cunneen, Martin & Mullins, Martin, 2021. "Surveillance and privacy – Beyond the panopticon. An exploration of 720-degree observation in level 3 and 4 vehicle automation," Technology in Society, Elsevier, vol. 66(C).
    6. Jiang, Like & Chen, Haibo & Chen, Zhiyang, 2022. "City readiness for connected and autonomous vehicles: A multi-stakeholder and multi-criteria analysis through analytic hierarchy process," Transport Policy, Elsevier, vol. 128(C), pages 13-24.
    7. Konstantinos Ntafloukas & Liliana Pasquale & Beatriz Martinez-Pastor & Daniel P. McCrum, 2023. "A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks," Future Internet, MDPI, vol. 15(3), pages 1-23, February.
    8. Nikitas, Alexandros & Parkinson, Simon & Vallati, Mauro, 2022. "The deceitful Connected and Autonomous Vehicle: Defining the concept, contextualising its dimensions and proposing mitigation policies," Transport Policy, Elsevier, vol. 122(C), pages 1-10.
    9. Mohamed Alawadhi & Jumah Almazrouie & Mohammed Kamil & Khalil Abdelrazek Khalil, 2020. "A systematic literature review of the factors influencing the adoption of autonomous driving," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1065-1082, December.
    10. Lee, Dasom & Hess, David J., 2020. "Regulations for on-road testing of connected and automated vehicles: Assessing the potential for global safety harmonization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 85-98.

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