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Connected and automated vehicles (CAVs) provide various valuable and advanced services to manufacturers, owners, mobility service providers, and transportation authorities. As a result, a large number of CAV applications have been proposed to improve the safety, mobility, and sustainability of the transportation system. With the increasing connectivity and automation, cybersecurity of the connected and automated transportation system (CATS) has raised attention to the transportation community in recent years. Vulnerabilities in CAVs can lead to breakdowns in the transportation system and compromise safety (e.g., causing crashes), performance (e.g., increasing congestion and reducing capacity), and fairness (e.g., vehicles fooling traffic signals). This paper presents our perspective on CATS cybersecurity via surveying recent pertinent studies focusing on the transportation system level, ranging from individual and multiple vehicles to the traffic network (including infrastructure). It also highlights threat analysis and risk assessment (TARA) tools and evaluation platforms, particularly for analyzing the CATS cybersecurity problem. Finally, this paper will provide valuable insights into developing secure CAV applications and investigating remaining open cybersecurity challenges that must be addressed. 

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.