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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Koley, Ipsita, Adhikary, Sunandan, Dey, Soumyajit
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 04.03.2021
Subjects
Adaptive systems
Cyber-physical systems
Dynamic systems theory
False alarms
Parameters
Safety critical
System theory
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Summary: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.
ISSN:2331-8422