ISCRAM: Supply Chain Risk Analysis and Mitigation Tool

University Transportation Centers Programhttps://doi.org/10.5281/zenodo.43102772020ZIPDatasetZhu, QuanyanKieras, TimothyFarooq, Muhammad JunaidConnected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)United States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyConnected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)United StatesAlgorithmsAutonomous vehiclesComputer securityConnected vehiclesCountermeasuresMobile communication systemsRisk assessmentVehicular ad hoc networks69A3551747124The research aims at developing a resilient framework to be applied to transportation systems using connected and autonomous vehicles (CAVs). This innovation potentially responds to accident rates often related to inefficient communication systems, supported by a variety of state-of-the-art safety applications. A Vehicular Ad hoc Network (VANET) is a self-organized, multi-purpose, service-oriented communication network enabling vehicle-to-vehicle and vehicle-to-roadside infrastructure communication for the purpose of exchanging messages to ensure an efficient and comfortable traffic system on roads. Its value, however, can potentially be impaired by cyberattacks. In particular, the focus of this research will be on false data injection attacks, in which a malicious agent aims at affecting CAV behavior by injecting in the network false information concerning, for example, the traffic condition in the area or the availability of charging stations for the CAVs. Computational and analytical frameworks are developed to assess cyber risks of connected and autonomous vehicles (CAVs) and develop detection techniques based on learning algorithms. Countermeasures are then developed using anomaly identification techniques based on the learning and detection algorithms.133