Quickest Detection of Adversarial Attacks Against Correlated Equilibria

TL;DR

This paper introduces a quickest detection framework for adversarial attacks on correlated equilibria in strategic games, using a generalized CUSUM scheme to detect attacks rapidly and limit utility loss.

Contribution

It models adversarial attacks as a zero-sum game and derives asymptotically optimal detection strategies using change detection theory.

Findings

Generalized CUSUM scheme effectively detects attacks in traffic routing games.

Proposed strategies balance attack impact and detectability.

Numerical results show utility loss is minimized by the detection scheme.

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.