An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems

2021/03/04 by Ipsita Koley, Koley, Ipsita, Sunandan Adhikary +3
Computer Science · Engineering · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Radiation Effects in Electronics #Smart Grid Security and Resilience

paper · pdf · doi:10.48550/arxiv.2103.02872

openalex publication_date 2021/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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