Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks

arXiv:2312.14625v2 Announce Type: replace-cross
Abstract: 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 …

abstract actors agent applications arxiv attacks collection cs.ai cs.cr cs.lg data data collection data-manipulation drivers exploit false information manipulation multi-agent navigation networks processing reinforcement reinforcement learning reliance services transportation type vulnerabilities