Reinforcement learning's contribution to the cyber security of distributed systems: Systematization of knowledge

Auteurs

Feltus C.

Référence

Research Anthology on Convergence of Blockchain, Internet of Things, and Security, pp. 421-444, 2022

Description

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.

Lien

doi:10.4018/978-1-6684-7132-6.ch025