The increasing reliance of drivers on navigation applications has made transportation networks more susceptible to data-manipulation attacks by malicious actors. Adversaries may exploit vulnerabilities in the data collection or processing of navigation services to inject false information, and to thus interfere with the drivers' route selection. Such attacks can significantly increase traffic congestions, resulting in substantial waste of time and resources, and may even disrupt essential services that rely on road networks. To assess the threat posed by such attacks, we introduce a computational framework to find worst-case data-injection attacks against transportation networks. First, we devise an adversarial model with a threat actor who can manipulate drivers by increasing the travel times that they perceive on certain roads. Then, we employ hierarchical multi-agent reinforcement learning to find an approximate optimal adversarial strategy for data manipulation. We demonstrate the applicability of our approach through simulating attacks on the Sioux Falls, ND network topology.

翻译：随着驾驶员对导航应用的日益依赖，交通网络更容易受到恶意行为者的数据操纵攻击。攻击者可能利用导航服务在数据采集或处理过程中的漏洞注入虚假信息，从而干扰驾驶员的路径选择。此类攻击会显著加剧交通拥堵，造成时间和资源的巨大浪费，甚至可能扰乱依赖道路网络的基础服务。为评估此类攻击的威胁，我们提出了一种计算框架，用于识别针对交通网络的最坏情况数据注入攻击。首先，我们设计了一种对抗模型，其中威胁行为者可通过增加驾驶员对某些道路感知行程时间的方式进行操纵。接着，我们采用分层多智能体强化学习来寻找近似最优的数据操纵对抗策略。通过在苏福尔斯（北达科他州）路网拓扑上的攻击模拟，我们验证了该方法的适用性。