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：驾驶员对导航应用的日益依赖使得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由选择。此类攻击会显著加剧交通拥堵，导致大量时间和资源浪费，甚至可能危及依赖道路网络的关键服务。为评估此类攻击造成的威胁，我们提出了一种用于识别交通网络最坏情况数据注入攻击的计算框架。首先，我们设计了一个对抗模型，其中威胁行为者可通过增加驾驶员在某些道路上感知的行车时间来操纵其行为。随后，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。通过在南达科他州苏福尔斯市网络拓扑上的攻击仿真，我们验证了该方法的应用可行性。