Computer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
Sci Rep. 2021 Mar 5;11(1):5329. doi: 10.1038/s41598-021-84291-w.
Disinformation continues to raise concerns due to its increasing threat to society. Nevertheless, a threat of a disinformation-based attack on critical infrastructure is often overlooked. Here, we consider urban traffic networks and focus on fake information that manipulates drivers' decisions to create congestion at a city scale. Specifically, we consider two complementary scenarios, one where drivers are persuaded to move towards a given location, and another where they are persuaded to move away from it. We study the optimization problem faced by the adversary when choosing which streets to target to maximize disruption. We prove that finding an optimal solution is computationally intractable, implying that the adversary has no choice but to settle for suboptimal heuristics. We analyze one such heuristic, and compare the cases when targets are spread across the city of Chicago vs. concentrated in its business district. Surprisingly, the latter results in more far-reaching disruption, with its impact felt as far as 2 km from the closest target. Our findings demonstrate that vulnerabilities in critical infrastructure may arise not only from hardware and software, but also from behavioral manipulation.
由于虚假信息对社会构成的威胁日益增加，虚假信息仍然令人担忧。然而，基于虚假信息的攻击对关键基础设施的威胁往往被忽视。在这里，我们考虑城市交通网络，并专注于操纵驾驶员决策以在城市范围内造成拥堵的虚假信息。具体来说，我们考虑了两种互补的场景，一种是说服驾驶员前往给定地点，另一种是说服他们离开该地点。我们研究了对手在选择要攻击的街道以最大程度地扰乱交通时所面临的优化问题。我们证明找到最优解在计算上是不可行的，这意味着对手别无选择，只能采用次优启发式算法。我们分析了这样的一种启发式算法，并比较了当目标分散在芝加哥市和集中在商业区时的情况。令人惊讶的是，后者会导致更广泛的破坏，其影响可波及离最近目标 2 公里远的地方。我们的研究结果表明，关键基础设施的漏洞不仅可能源于硬件和软件，还可能源于行为操纵。
