Abstract
Autonomous systems are soon expected to integrate into our lives as home assistants, delivery drones, and driverless cars. Given the importance of cybersecurity of these systems, game-theoretic solutions provide a mathematical modeling of the interaction between malicious users and the system. This work introduces a novel framework for analyzing potential denial of service (DoS) attacks on autonomous vehicular networks by integrating a non-cooperative, non-zero-sum game model with a comprehensive simulation environment (VEINS/OMNET++/SUMO). Our key contribution lies in the systematic quantification of attacker and defender payoffs based on realistic network metrics, enabling the identification of optimal strategies across 21 distinct attack/defense scenarios derived from tunable network parameters. We specifically emphasize the model’s ability to provide a predictive, all-scenario evaluation to aid system designers in preparing for real-time cyber attacks on driverless cars.
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