The growing complexity and interconnectivity of Intelligent Transportation Systems (ITS) make them increasingly vulnerable to advanced cyber threats, particularly deceptive information attacks. These sophisticated threats exploit vulnerabilities to manipulate data integrity and decision-making processes through techniques such as data poisoning, spoofing, and phishing. They target multiple ITS domains, including intra-vehicle systems, inter-vehicle communications, transportation infrastructure, and human interactions, creating cascading effects across the ecosystem. This chapter introduces a game-theoretic framework, enhanced by control and learning theories, to systematically analyze and mitigate these risks. By modeling the strategic interactions among attackers, users, and system operators, the framework facilitates comprehensive risk assessment and the design of adaptive, scalable resilience mechanisms. A prime example of this approach is the Proactive Risk Assessment and Mitigation of Misinformed Demand Attacks (PRADA) system, which integrates trust mechanisms, dynamic learning processes, and multi-layered defense strategies to counteract deceptive attacks on navigational recommendation systems. In addition, the chapter explores the broader applicability of these methodologies to address various ITS threats, including spoofing, Advanced Persistent Threats (APTs), and denial-of-service attacks. It highlights cross-domain resilience strategies, offering actionable insights to bolster the security, reliability, and adaptability of ITS. By providing a robust game-theoretic foundation, this work advances the development of comprehensive solutions to the evolving challenges in ITS cybersecurity.

翻译：智能交通系统日益增长的复杂性和互联性使其越发容易受到高级网络威胁的影响，尤其是欺骗性信息攻击。这类复杂威胁利用系统漏洞，通过数据投毒、欺骗和网络钓鱼等技术手段，操纵数据完整性与决策过程。它们针对智能交通系统的多个领域，包括车内系统、车际通信、交通基础设施以及人机交互，在整个生态系统中产生连锁效应。本章引入一个由控制与学习理论增强的博弈论框架，以系统性地分析和缓解这些风险。通过建模攻击者、用户和系统运营商之间的策略互动，该框架支持全面的风险评估，并促进自适应、可扩展的韧性机制设计。该方法的一个典型实例是主动风险评估与误导需求攻击缓解系统，该系统整合信任机制、动态学习过程和多层防御策略，以应对导航推荐系统中的欺骗性攻击。此外，本章探讨了这些方法在应对各类智能交通系统威胁（包括欺骗攻击、高级持续性威胁和拒绝服务攻击）中的更广泛适用性。文章重点阐述了跨领域韧性策略，为增强智能交通系统的安全性、可靠性和适应性提供了可操作的见解。通过建立坚实的博弈论基础，本研究推动了针对智能交通系统网络安全不断演变挑战的全面解决方案的发展。