Hybrid Quantum Artificial Intelligence Electromagnetic Spectrum Analysis Framework for Transportation System Security

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Journal of Hardware and Systems Security

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The traffic signal control (TSC) system is faced with both opportunities and challenges as a consequence of connected vehicle (CV) technology. Although implementing CV technology might considerably enhance safety and mobility performance, the connection between vehicles and transportation infrastructure may raise the dangers of cyberattacks. Studies on cybersecurity in TSC systems have been undertaken in recent years. However, a safe framework for air-gapped malware analysis is still lacking. Our study aims to close this research gap by presenting a thorough electromagnetic analysis approach to address the cybersecurity issue facing the TSC in the CV environment. In this technique, a hybrid deep learning architecture based on classical quantum transfer learning models is constructed to study electromagnetic (EM) spectra. We assess the effect of adversarial attacks on TSC systems using these hybrid models and distinguish attacks from normal operations of the controller. A neural network trained on a dataset to collect pertinent characteristics from a high-dimensional dataset of electromagnetic (EM) trace-based TSC attack vectors form the basis of hybrid models. The quantum layer then examines the outcome of the standard deep learning process. A number of quantum gates make up the quantum layer, which can enable a number of quantum mechanical activities, including superposition and entanglement. The most conceivable and feasible attack approach in transport signal controllers has been found to be data spoofing, and the potential of the proposed air-gapped electromagnetic Hybrid classical quantum monitoring framework in sensor fusion and interrupts is explored in light of these findings. A range of sensor fusion configurations and interruptions have been investigated to identify a fake data attack from a normal controller operation. With lower penetration rates of 1% to 10%, the prediction accuracy of a four-way controller ranges from 76% to 84% and is in the low 80% for a two-way controller. There are fewer false alarms when the penetration rate is higher since there is greater confidence in the prediction. Further testing of different timing scheme modifications in a four-way controller operation yielded a prediction accuracy of 88% when the time duration was raised by more than 60% in all circumstances.