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Connected and autonomous vehicles: a cyber-risk classification framework
Sheehan, Barry; Murphy, Finbarr; Mullins, Martin; Ryan, Cian
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. PUBLISHED peer-reviewed
Keyword(s): Auto insurance; Bayesian networks; Connected and autonomous vehicles; Cyber liability; Cyber-risk; Intelligent transport systems; Risk assessment
Publication Date:
2018
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): 609772
Transporation Research Part A;
690772
Publisher(s): Elsevier
First Indexed: 2019-03-06 06:34:45 Last Updated: 2019-03-06 06:34:45