Transportation networks are considered one of the critical physical infrastructures for resilient cities (cyber-physical systems). In efforts to minimize adverse effects that come with the advancement of vehicular technologies, various governmental agencies, such as the U.S. Department of Homeland Security and the National Highway Traffic Safety Administration (NHTSA), work together. This paper develops belief-network-based attack modeling at signalized traffic networks under connected vehicle and intelligent signals frameworks. For different types of cyber attacks, defined in the literature, risk areas and impacts of attacks are evaluated. Vulnerability scores, technically based on the selected metrics, are calculated for signal controllers. In addition, the effect of having redundant traffic sensing systems on intersection performance measures is demonstrated in terms of average queue length differences.Resilience of critical infrastructures is defined as their ability to withstand an upsetting event, deliver essential levels of service during it, and recover quickly after it. With the increase of connected systems, cyber attacks that can target critical infrastructure systems are becoming more troubling. Transportation networks are considered one of the critical physical infrastructures for resilient cities (cyber-physical systems [CPSs] [1]). According to recent reports (2-4), several benefits are foreseen from upcoming technologies such as connected and autonomous vehicles (CAVs), including up to 80% reduction in fatalities from multi-vehicle crashes and prevention of the majority of human-error-related incidents, which takes out about 94% of all incidents. These intelligent applications, however, come at a price; for example, in 2015 alone 1.5 million vehicles were recalled because of cybersecurity vulnerabilities. NHTSA's current research focuses on CAVs that are heavily involved in secure implementations which will enable the field and its technology experts to harness efficient, reliable, and secure system design (3). Some of these topics can be listed as anomaly-based intrusion detection systems, cybersecurity of firmware updates, cybersecurity on heavy vehicles, vehicle-to-vehicle (V2V) communication interfaces, and trusted vehicle-to-everything (V2X) communications (5). The main goals for any critical infrastructure are quick detection of attacks and rapid mitigation efforts (6). There are many attack types, of which some can be resolved via detection and some require redundant systems and sensors. In intelligent transportation systems (ITS), to increase security and resiliency in case of possible attacks or benign system errors during different events, research is likewise needed into detection using various sensors and data types. Research is also necessary to enhance confidence in sensor readings by checking consistency with other sensors and information sources as well as validating control system commands (7). This paper investigates attack modeling and impacts on intelligent signals. For differe...

<p>Controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. In this study, we developed a hybrid approach for CAN intrusion detection using a classical convolutional neural network (CCNN) and a quantum restricted Boltzmann machine (quantum RBM). The CCNN is dedicated for feature extraction from CAN images generated from a vehicle’s CAN bus data, while the quantum RBM is dedicated for CAN image reconstruction for a classification-based intrusion detection. To evaluate the performance of the hybrid approach, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid approach to a similar but classical-only approach. Our analyses showed that the hybrid approach performs better in CAN intrusion detection compared to the classical-only approach. For the three datasets considered in this study, the best models in the hybrid approach achieved 97.5%, 97%, and 98.3% intrusion detection accuracies, and 94.7%, 93.9%, and 97.2% recall, respectively, whereas the best models in the classical-only approach achieved 86.7%, 95%, and 89.7% intrusion detection accuracies, and 70.7%, 89.8, and 80.6% recall, respectively.</p>

<p>Controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. In this study, we developed a hybrid approach for CAN intrusion detection using a classical convolutional neural network (CCNN) and a quantum restricted Boltzmann machine (quantum RBM). The CCNN is dedicated for feature extraction from CAN images generated from a vehicle’s CAN bus data, while the quantum RBM is dedicated for CAN image reconstruction for a classification-based intrusion detection. To evaluate the performance of the hybrid approach, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid approach to a similar but classical-only approach. Our analyses showed that the hybrid approach performs better in CAN intrusion detection compared to the classical-only approach. For the three datasets considered in this study, the best models in the hybrid approach achieved 97.5%, 97%, and 98.3% intrusion detection accuracies, and 94.7%, 93.9%, and 97.2% recall, respectively, whereas the best models in the classical-only approach achieved 86.7%, 95%, and 89.7% intrusion detection accuracies, and 70.7%, 89.8, and 80.6% recall, respectively.</p>