Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
This addresses a security vulnerability in navigation systems that affects drivers and urban infrastructure, though it is incremental as it builds on existing multi-agent reinforcement learning methods.
The paper tackles the problem of false-data injection attacks on transportation networks by introducing a computational framework to find worst-case attacks, demonstrating through simulation on the Sioux Falls network that such attacks can significantly increase traffic congestion.
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.