用强化学习对抗虚假交通数据攻击，提升车联网路由安全。
Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
- 构建攻防双方的零和博弈模型，用多智能体强化学习求解纳什均衡。
- 在攻击下仍能将总通行时间控制在最坏情况边界内，显著优于基线方法。
- 适用于需要高鲁棒性的智能交通系统，尤其适合防御协同式虚假数据攻击。
在现代交通网络中，攻击者可通过虚假数据注入攻击（如利用多个设备运行众包导航应用模拟拥堵）操纵路由算法，误导车辆选择次优路径并加剧拥堵。为应对这一威胁，我们建立攻击者与防御者之间的零和博弈模型：攻击者注入扰动，防御者基于边上的观测通行时间检测异常。提出一种基于多智能体强化学习的计算方法，求解该博弈的纳什均衡，获得最优检测策略，确保即使在攻击存在时，总通行时间仍保持在最坏情况边界内。实验表明，该方法可得到近似均衡策略，且在攻防双方均显著优于基线方法，为提升交通网络韧性提供了有效框架。
原文摘要 · Abstract (English)
In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To address these threats, we formulate a strategically zero-sum game between an attacker, who injects such perturbations, and a defender, who detects anomalies based on the observed travel times of network edges. We propose a computational method based on multi-agent reinforcement learning to compute a Nash equilibrium of this game, providing an optimal detection strategy, which ensures that total travel time remains within a worst-case bound, even in the presence of an attack. We present an extensive experimental evaluation that demonstrates the robustness and practical benefits of our approach, providing a powerful framework to improve the resilience of transportation networks against false data injection. In particular, we show that our approach yields approximate equilibrium policies and significantly outperforms baselines for both the attacker and the defender.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。