基于多代理强化学习与代理间交互的物联网动态网络入侵检测分散方法

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS

AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2024-02-29 DOI:10.3103/S0146411623080096

M. O. Kalinin, E. I. Tkacheva

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引用次数: 0

摘要

摘要研究了如何应用多代理强化学习技术解决物联网（IoT）系统中的入侵检测问题。实现了多代理入侵检测系统的三种模型：完全分散的系统、预报数据传输系统和观测数据传输系统。实验结果与 Suricata 开码入侵检测系统进行了比较。实验结果表明，所考虑的多代理系统架构不存在现有解决方案的缺陷。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

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A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

The application of multiagent reinforcement learning technology to solve the problem of intrusion detection in the Internet of Things (IoT) systems is considered. Three models of a multiagent intrusion detection system are implemented: a completely decentralized system, a system with the transfer of forecast data, and a system with the transfer of observation data. The experimental results are given in comparison with the Suricata open-code intrusion detection system. The considered architectures of multiagent systems are shown to be free from the shortcomings of the existing solutions.

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来源期刊

AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.70

自引率

22.20%

发文量

期刊介绍： Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision