Securing Intelligent Transportation Systems: A Dual-Framework Approach for Privacy Protection and Cybersecurity Using Generative AI

Integrating Generative AI (GenAI) into Intelligent Transportation Systems (ITS) raises both enormous opportunities and major worries, especially in the areas of privacy and cybersecurity, which are already at the forefront of these developments. Developing and implementing robust security measures to secure sensitive data and address new cyber threats is of utmost importance, especially with the growing dependence on AI technology in transportation networks. This article looks at GenAI and how it may improve ITS intelligence and efficiency while addressing the risks of using it a lot. It delves into the difficulties of protecting AI-driven systems against hostile assaults (AI-MA), particularly emphasizing transportation infrastructure security, intrusion detection, and data privacy. The research stresses the significance of modern encryption methods, real-time monitoring threats, and adaptive security frameworks to ensure ITS are secure and resilient. In addition, it delves into how transportation systems are affected by ever-changing cyber threats, offering proactive security solutions to combat these dangers and strengthen ITS. This paper’s overarching goal is to lay out a course of action for integrating GenAI into ITS in a way that strikes a good balance between fostering innovation and ensuring privacy and security via thorough analysis. The proposed AI-MA model achieves a high threat detection accuracy of 96.2%, a privacy protection score of 91.8%, a computational efficiency of 92.9%, a resilience score of 97.8%, and a network reliability ratio of 92.6% compared to other existing models.