Resilience-by-design in Adaptive Multi-Agent Traffic Control Systems

TL;DR

This paper analyzes security vulnerabilities in adaptive traffic control systems caused by Sybil attacks from connected vehicles and proposes a game-theoretic mitigation method to enhance system resilience, demonstrating significant improvements in traffic management robustness.

Contribution

It introduces the first detailed security analysis of Sybil attacks on adaptive traffic systems and develops a novel game-theoretic mitigation approach to improve their resilience.

Findings

Sybil attacks can cause approximately 48.9% increase in time loss at intersections.

The proposed mitigation significantly enhances traffic system robustness.

Extensive experiments validate the effectiveness of the mitigation strategy.

Abstract

Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITS), such as Traffic Signal Control (TSC) for urban traffic congestion management. However, their involvement will expand the space of security vulnerabilities and create larger threat vectors. In this paper, we perform the first detailed security analysis and implementation of a new cyber-physical attack category carried out by the network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC), namely, coordinated Sybil attacks, where vehicles with forged or fake identities try to alter the data collected by the AMATSC algorithms to sabotage their decisions. Consequently, a novel, game-theoretic mitigation approach at the application layer is proposed to minimize the impact of such sophisticated data corruption attacks. The devised minimax game model enables the AMATSC algorithm to generate optimal decisions under a suspected attack, improving its resilience. Extensive experimentation is performed on a traffic dataset provided by the City of Montreal under real-world intersection settings to evaluate the attack impact. Our results improved time loss on attacked intersections by approximately 48.9%. Substantial benefits can be gained from the mitigation, yielding more robust adaptive control of traffic across networked intersections.