Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems: Systematization of Knowledge

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Author(s): Christophe Feltus (Luxembourg Institute of Science and Technology, Luxembourg)
Copyright: 2023
Pages: 24
Source title: Research Anthology on Convergence of Blockchain, Internet of Things, and Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7132-6.ch025

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

Reinforcement learning (RL) is a machine learning paradigm, like supervised or unsupervised learning, which learns the best actions an agent needs to perform to maximize its rewards in a particular environment. Research into RL has been proven to have made a real contribution to the protection of cyberphysical distributed systems. In this paper, the authors propose an analytic framework constituted of five security fields and eight industrial areas. This framework allows structuring a systematic review of the research in artificial intelligence that contributes to cybersecurity. In this contribution, the framework is used to analyse the trends and future fields of interest for the RL-based research in information system security.

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Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems: Systematization of Knowledge

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