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.

翻译：内部人员被定义为团队中表面上维持合作姿态，但暗中偏离团队最优协同控制策略以追求私人目标的成员。此类内部威胁可能严重破坏协同系统：微妙的偏离会降低集体性能、危及任务成功率并损害操作安全性。本文提出一个用于识别和缓解协同控制环境中内部威胁的综合性框架。我们引入一种内部人员感知的博弈论建模方法，其中内部人员的隐藏意图被参数化，使得威胁识别任务可重构为参数估计问题。为应对这一挑战，我们采用在线间接双重自适应控制方法，该方法同时推断内部人员的控制策略并抵消其负面影响。通过注入合理设计的探测信号，所提出的缓解策略渐近恢复名义最优控制律——即在完全知晓内部人员目标情况下可实现的控制律。仿真结果验证了所提出的识别-缓解框架的有效性，并证明其在存在隐蔽对抗行为时仍能保持团队性能的能力。