  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.
