Cyber-security Aware Traffic Flow Modeling and Data Processing Power Optimization

Abstract

The growing deployment of connected and automated vehicles (CAVs) introduces new opportunities and challenges at the intersection of traffic engineering and cybersecurity. While CAVs leverage onboard sensors and computing to navigate their environment, the integration of vehicle to vehicle (V2V) and vehicle-to-infrastructure (V2I) communications has elevated expectations for cooperation, safety, and efficiency. This connectivity, however, introduces system-level vulnerabilities that are not present in isolated autonomous vehicles. In this thesis, we develop a queueing-based analytical framework to examine how cyber-induced communication loads and adversarial message infiltration influence the real-time processing of detection and control signals within CAVs. By formulating and solving optimization problems for resource allocation, we identify how limited computational capacity should be divided between detection and decision-making tasks to maintain traffic performance in adversarial environments. The model derives closed-form relationships between processing rates and macroscopic traffic variables such as delay, flow, and speed. Numerical experiments reveal critical trade-offs, as malicious message share increases, the system must prioritize defensive processing at the cost of responsiveness, leading to changes in traffic efficiency. Our findings emphasize the need for cybersecurity-aware traffic flow models and provide operational insights into the design of resilient and adaptive cooperative driving systems.