Game-Theoretic Learning-Based Mitigation of Insider Threats

TL;DR

This paper introduces a game-theoretic, adaptive control framework to identify and mitigate insider threats in cooperative systems, ensuring team performance despite covert adversarial actions.

Contribution

It presents a novel insider-aware, game-theoretic formulation combined with online adaptive control to detect and counteract insider deviations in cooperative control settings.

Findings

Effective identification and mitigation of insider threats demonstrated in simulations.

Asymptotic recovery of optimal control law under covert adversarial behavior.

Framework maintains team performance despite insider deviations.

Abstract

An insider is defined as a team member who covertly deviates from the team's optimal collaborative control strategy in pursuit of a private objective, while maintaining an outward appearance of cooperation. Such insider threats can severely undermine cooperative systems: subtle deviations may degrade collective performance, jeopardize mission success, and compromise operational safety. This paper presents a comprehensive framework for identifying and mitigating insider threats in cooperative control settings. We introduce an insider-aware, game-theoretic formulation in which the insider's hidden intention is parameterized, allowing the threat identification task to be reformulated as a parameter estimation problem. To address this challenge, we employ an online indirect dual adaptive control approach that simultaneously infers the insider's control strategy and counteracts its negative influence. By injecting properly designed probing signals, the resulting mitigation policy asymptotically recovers the nominal optimal control law - one that would be achieved under full knowledge of the insider's objective. Simulation results validate the effectiveness of the proposed identification-mitigation framework and illustrate its capability to preserve team performance even in the presence of covert adversarial behavior.