2018/08/24 by Áron Lászka, Laszka, Aron, Waseem Abbas +5 · 1 citation
Computer Science · Engineering · #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Network Security and Intrusion Detection #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.1808.08349
openalex publication_date 2018/08/24 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
In recent years, state-of-the-art traffic-control devices have evolved from\nstandalone hardware to networked smart devices. Smart traffic control enables\noperators to decrease traffic congestion and environmental impact by acquiring\nreal-time traffic data and changing traffic signals from fixed to adaptive\nschedules. However, these capabilities have inadvertently exposed traffic\ncontrol to a wide range of cyber-attacks, which adversaries can easily mount\nthrough wireless networks or even through the Internet. Indeed, recent studies\nhave found that a large number of traffic signals that are deployed in practice\nsuffer from exploitable vulnerabilities, which adversaries may use to take\ncontrol of the devices. Thanks to the hardware-based failsafes that most\ndevices employ, adversaries cannot cause traffic accidents directly by setting\ncompromised signals to dangerous configurations. Nonetheless, an adversary\ncould cause disastrous traffic congestion by changing the schedule of\ncompromised traffic signals, thereby effectively crippling the transportation\nnetwork. To provide theoretical foundations for the protection of\ntransportation networks from these attacks, we introduce a game-theoretic model\nof launching, detecting, and mitigating attacks that tamper with traffic-signal\nschedules. We show that finding optimal strategies is a computationally\nchallenging problem, and we propose efficient heuristic algorithms for finding\nnear optimal strategies. We also introduce a Gaussian-process based anomaly\ndetector, which can alert operators to ongoing attacks. Finally, we evaluate\nour algorithms and the proposed detector using numerical experiments based on\nthe SUMO traffic simulator.\n