Increased dependence on networked, software based control has escalated the vulnerabilities of Cyber Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of detection systems. In the present work, we propose a Reinforcement Learning (RL) based framework which adaptively sets the parameters of such detectors based on experience learned from attack scenarios, maximizing detection rate and minimizing false alarms in the process while attempting performance preserving control actions.

中文翻译：

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基于RL的自适应检测策略可保护网络物理系统

对网络化，基于软件的控制的依赖性越来越大，从而加剧了网络物理系统（CPS）的漏洞。利用动态系统理论开发的检测和监视组件通常被用作轻型安全措施，以保护此类安全关键的CPS免受虚假数据注入攻击。但是，现有方法并未将攻击场景与检测系统的参数相关联。在当前的工作中，我们提出了一个基于强化学习（RL）的框架，该框架可根据从攻击场景中获悉的经验来自适应地设置此类检测器的参数，在尝试保留性能的同时，最大程度地提高检测率并最大程度地减少错误警报。