モデルフリー強化学習によるステルス攻撃に耐える安全計画
Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
制御信号への攻撃を確率的ゲームとして定式化し、環境が未知でもモデルフリー強化学習でLTL仕様を満たす安全な計画を学習する手法を提案した。
著者: Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic
分類: cs.RO, cs.GT
原文アブストラクト
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the attacker as an agent who has the full knowledge of the controller as well as the employed intrusion-detection system and who wants to prevent the controller from performing tasks while staying stealthy. We formulate the problem as a stochastic game between the attacker and the controller and present an approach to express the objective of such an agent and the controller as a combined linear temporal logic (LTL) formula. We then show that the planning problem, described formally as the problem of satisfying an LTL formula in a stochastic game, can be solved via model-free reinforcement learning when the environment is completely unknown. Finally, we illustrate and evaluate our methods on two robotic planning case studies.
