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Journal Articles Information Year : 2022

Finding Optimal Moving Target Defense Strategies: A Resilience Booster for Connected Cars

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Abstract

During their life-cycle, modern connected cars will have to face various and changing security threats. As for any critical embedded system, security fixes in the form of software updates need to be thoroughly verified and cannot be deployed on a daily basis. The system needs to commit to a defense strategy, while attackers can examine vulnerabilities and prepare possible exploits before attacking. In order to break this asymmetry, it can be advantageous to use proactive defenses, such as reconfiguring parts of the system configuration. However, resource constraints and losses in quality of service need to be taken into account for such Moving Target Defenses (MTDs). In this article, we present a game-theoretic model that can be used to compute an optimal MTD defense for a critical embedded system that is facing several attackers with different objectives. The game is resolved using off-the-shelf MILP solvers. We validated the method with an automotive use case and conducted extensive experiments to evaluate its scalability and stability.

Dates and versions

hal-03670106 , version 1 (17-05-2022)

Identifiers

Cite

Maxime Ayrault, Ulrich Kühne, Etienne Borde. Finding Optimal Moving Target Defense Strategies: A Resilience Booster for Connected Cars. Information, 2022, 13 (5), pp.242. ⟨10.3390/info13050242⟩. ⟨hal-03670106⟩
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