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Recent advances in wireless technologies have enabled many new applicationsin Intelligent Transportation Systems (ITS) such as collision avoidance,cooperative driving, congestion avoidance, and traffic optimization. Due to thevulnerable nature of wireless communication against interference andintentional jamming, ITS face new challenges to ensure the reliability and thesafety of the overall system. In this paper, we expose a class of stealthyattacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion byexploiting how drivers make decisions based on smart traffic signs. An attackermounting a SiT attack solves a Markov Decision Process problem to findoptimal/suboptimal attack policies in which he/she interferes with awell-chosen subset of signals that are based on the state of the system. Weapply Approximate Policy Iteration (API) algorithms to derive potent attackpolicies. We evaluate their performance on a number of systems and compare themto other attack policies including random, myopic and DoS attack policies. Thegenerated policies, albeit suboptimal, are shown to significantly outperformother attack policies as they maximize the expected cumulative reward from thestandpoint of the attacker.

Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy Attacks that Cause Traffic Congestion