An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems
This work addresses security vulnerabilities in safety-critical CPSs, offering an incremental improvement by correlating attack scenarios with detection parameters.
The paper tackles the problem of securing Cyber-Physical Systems against false data injection attacks by proposing a Reinforcement Learning-based framework that adaptively sets detector parameters based on attack scenarios, resulting in maximized detection rates and minimized false alarms while preserving control performance.
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.