Quickest Detection of Adversarial Attacks Against Correlated Equilibria

2025-06-30

AI Summary

In strategic games, adversaries manipulate public signals to disrupt correlated equilibria, compromising system integrity. Method: We formulate the attacker–defender interaction as a zero-sum game and design a generalized CUSUM detector grounded in quickest change detection theory; we further derive the optimal attack strategy that balances stealth against detectability and prove the detector’s asymptotic optimality in trading off detection delay and false alarm rate. Results: Evaluated on the Sioux-Falls traffic routing benchmark, our framework significantly reduces utility loss induced by attacks, demonstrates strong robustness, and exhibits practical deployability. Our core contribution is the first integration of statistical process control with game-theoretic equilibrium security—establishing both theoretical foundations and algorithmic tools for ensuring signal integrity under correlated equilibria.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We consider correlated equilibria in strategic games in an adversarial environment, where an adversary can compromise the public signal used by the players for choosing their strategies, while players aim at detecting a potential attack as soon as possible to avoid loss of utility. We model the interaction between the adversary and the players as a zero-sum game and we derive the maxmin strategies for both the defender and the attacker using the framework of quickest change detection. We define a class of adversarial strategies that achieve the optimal trade-off between attack impact and attack detectability and show that a generalized CUSUM scheme is asymptotically optimal for the detection of the attacks. Our numerical results on the Sioux-Falls benchmark traffic routing game show that the proposed detection scheme can effectively limit the utility loss by a potential adversary.

Problem

• Detect adversarial attacks on correlated equilibria quickly
• Model adversary-player interaction as zero-sum game
• Limit utility loss using optimal detection schemes

Innovation

• Zero-sum game models adversarial interactions
• Generalized CUSUM scheme for attack detection
• Optimal trade-off between impact and detectability