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

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.

Distributed System Load Balancing Strategy for Converged Workstations

“…The performance of the DS LB algorithm depends to a large extent on the selection of evaluation indicators [15][16]. A good evaluation index can enable each node to work under normal load status, without making some nodes overloaded or too low, so that the system's operating status and file processing performance can be optimized.…”