Digital Twins and AI-Driven Cybersecurity for Intelligent Transportation Networks

Intelligent Transportation Networks have evolved to be complex cyber-physical networks in which automation, data exchange and real-time connectivity are in tandem for enhanced mobility and safety. Nevertheless, information technology causes an elevated level of digital interdependence and thus makes ITS infrastructures ever more prone to the sophisticated cyber threat. This paper is a unified architecture that combines the Digital Twin (DT) paradigm and Artificial Intelligence (AI) and proposes to strengthen concepts of cybersecurity and resilience in smart transportation systems. By leveraging the power of Digital Twins for real time simulation and AI based adaptive threat detection, the proposed method creates a predictive, self-healing defence mechanism that can combat the changing nature of evolving cyber-attacks for different vehicular and infrastructural domains. The paper presents a synthesis of recent developments in DT-enabled security, explores blockchain-based approaches to data integrity and outlines some of the major research challenges for achieving secure, intelligent, and sustainable transportation systems.

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