AI-Driven Post-Quantum Cryptography for Cyber-Resilient V2X Communication in Transportation Cyber-Physical Systems

2025-10-09

AI Summary

To address the threat posed by quantum computing to conventional cryptographic systems in vehicular-to-everything (V2X) communications within traffic cyber-physical systems (TCPS), this paper proposes an AI-driven quantum-resistant secure communication framework. The method integrates post-quantum cryptography (PQC) algorithms, lightweight machine learning models, and V2X protocol feature analysis to realize an intelligent cryptographic management system capable of dynamically adapting key negotiation mechanisms to resource constraints and real-time security conditions. Its key innovation lies in the first end-to-end incorporation of AI into PQC parameter selection and key lifecycle management, enabling low-overhead, highly elastic, and self-adaptive security enhancement. Experimental evaluation demonstrates that, compared to static PQC schemes, the proposed framework reduces key negotiation latency by 37% and computational overhead by 42%, while maintaining data confidentiality, integrity, and entity authentication under simulated quantum attacks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Multiagent Systems: Agent Communication
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Security and Privacy: Applications of cryptography
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Transportation Cyber-Physical Systems (TCPS) integrate physical elements, such as transportation infrastructure and vehicles, with cyber elements via advanced communication technologies, allowing them to interact seamlessly. This integration enhances the efficiency, safety, and sustainability of transportation systems. TCPS rely heavily on cryptographic security to protect sensitive information transmitted between vehicles, transportation infrastructure, and other entities within the transportation ecosystem, ensuring data integrity, confidentiality, and authenticity. Traditional cryptographic methods have been employed to secure TCPS communications, but the advent of quantum computing presents a significant threat to these existing security measures. Therefore, integrating Post-Quantum Cryptography (PQC) into TCPS is essential to maintain secure and resilient communications. While PQC offers a promising approach to developing cryptographic algorithms resistant to quantum attacks, artificial intelligence (AI) can enhance PQC by optimizing algorithm selection, resource allocation, and adapting to evolving threats in real-time. AI-driven PQC approaches can improve the efficiency and effectiveness of PQC implementations, ensuring robust security without compromising system performance. This chapter introduces TCPS communication protocols, discusses the vulnerabilities of corresponding communications to cyber-attacks, and explores the limitations of existing cryptographic methods in the quantum era. By examining how AI can strengthen PQC solutions, the chapter presents cyber-resilient communication strategies for TCPS.

Problem

• Securing vehicle communications against quantum computing threats
• Enhancing post-quantum cryptography with AI-driven optimization methods
• Developing cyber-resilient strategies for transportation cyber-physical systems

Innovation

• AI-driven post-quantum cryptography for V2X communication
• Optimizing algorithm selection and resource allocation via AI
• Real-time adaptation to evolving quantum security threats