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  1. Connected vehicle (CV) technology brings both opportunities and challenges to the traffic signal control (TSC) system. While safety and mobility performance could be greatly improved by adopting CV technologies, the connectivity between vehicles and transportation infrastructure may increase the risks of cyber threats. In the past few years, studies related to cybersecurity on the TSC systems were conducted. However, there still lacks a systematic investigation that provides a comprehensive analysis framework. In this study, our aim is to fill the research gap by proposing a comprehensive analysis framework for the cybersecurity problem of the TSC in the CV environment. With potential threats towards the major components of the system and their corresponding impacts on safety and efficiency analyzed, data spoofing attack is considered the most plausible and realistic attack approach. Based on this finding, different attack strategies and defense solutions are discussed. A case study is presented to show the impact of the data spoofing attacks towards a selected CV based TSC system and corresponding mitigation countermeasures. This case study is conducted on a hybrid security testing platform, with virtual traffic and a real V2X communication network. To the best of our knowledge, this is the first study to present a comprehensive analysis framework to the cybersecurity problem of the CV-based TSC systems. 
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  2. Connected vehicle (CV) technologies enable data exchange between vehicles and transportation infrastructure. In a CV environment, traffic signal control systems receive CV trajectory data through vehicle-to-infrastructure (V2I) communications to make control decisions. Comparing with existing data collection methods (e.g., from loop-detectors), the CV trajectory data provide much richer information, and therefore have great potentials to improve the system performance by reducing total vehicle delay at signalized intersections. However, this connectivity might also bring cyber security concerns. In this paper, we aim to investigate the security problem of CV-based traffic signal control (CV-TSC) systems. Specifically, we focus on evaluating the impact of falsified data attacks on the system performance. A black-box attack scenario, in which the control logic of a CV-TSC system is unavailable to attackers, is considered. A two-step attack model is constructed. In the first step, the attacker tries to learn the control logic using a surrogate model. Based on the surrogate model, in the second step, the attacker launches falsified data attacks to influence the control systems to make sub-optimal control decisions. In the case study, we apply the attack model to an existing CV-TSC system (i.e., I-SIG) and find intersection delay can be significantly increased. Finally, we discuss some promising defense directions. 
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