Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy Attacks that Cause Traffic Congestion

TL;DR

This paper introduces a framework for stealthy traffic congestion attacks in ITS, modeling attacker strategies with Markov Decision Processes and demonstrating their effectiveness over other attack methods.

Contribution

It presents a novel class of stealthy traffic attacks (SiT) using MDPs and API algorithms to optimize attack policies against intelligent transportation systems.

Findings

Attack policies outperform random and myopic strategies.

Stealthy attacks significantly increase traffic congestion.

Optimal attack policies maximize attacker rewards.

Abstract

Recent advances in wireless technologies have enabled many new applications in Intelligent Transportation Systems (ITS) such as collision avoidance, cooperative driving, congestion avoidance, and traffic optimization. Due to the vulnerable nature of wireless communication against interference and intentional jamming, ITS face new challenges to ensure the reliability and the safety of the overall system. In this paper, we expose a class of stealthy attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by exploiting how drivers make decisions based on smart traffic signs. An attacker mounting a SiT attack solves a Markov Decision Process problem to find optimal/suboptimal attack policies in which he/she interferes with a well-chosen subset of signals that are based on the state of the system. We apply Approximate Policy Iteration (API) algorithms to derive potent attack policies. We evaluate their performance on a number of systems and compare them to other attack policies including random, myopic and DoS attack policies. The generated policies, albeit suboptimal, are shown to significantly outperform other attack policies as they maximize the expected cumulative reward from the standpoint of the attacker.