A Survey on Multi-Agent Based Collaborative Intrusion Detection Systems

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nassima Bougueroua, S. Mazouzi, Mohamed Belaoued, N. Seddari, A. Derhab, A. Bouras
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引用次数: 11

Abstract

Abstract Multi-Agent Systems (MAS) have been widely used in many areas like modeling and simulation of complex phenomena, and distributed problem solving. Likewise, MAS have been used in cyber-security, to build more efficient Intrusion Detection Systems (IDS), namely Collaborative Intrusion Detection Systems (CIDS). This work presents a taxonomy for classifying the methods used to design intrusion detection systems, and how such methods were used alongside with MAS in order to build IDS that are deployed in distributed environments, resulting in the emergence of CIDS. The proposed taxonomy, consists of three parts: 1) general architecture of CIDS, 2) the used agent technology, and 3) decision techniques, in which used technologies are presented. The proposed taxonomy reviews and classifies the most relevant works in this topic and highlights open research issues in view of recent and emerging threats. Thus, this work provides a good insight regarding past, current, and future solutions for CIDS, and helps both researchers and professionals design more effective solutions.
基于多Agent的协同入侵检测系统综述
摘要多智能体系统(MAS)在复杂现象建模与仿真、分布式问题求解等领域得到了广泛的应用。同样,MAS已被用于网络安全,以建立更有效的入侵检测系统(IDS),即协同入侵检测系统(CIDS)。这项工作提出了一种分类法,用于对用于设计入侵检测系统的方法进行分类,以及如何将这些方法与MAS一起使用,以构建部署在分布式环境中的入侵检测系统,从而导致了入侵检测系统的出现。该分类法由三部分组成:1)CIDS的总体体系结构;2)所使用的代理技术;3)决策技术,其中介绍了所使用的技术。所提出的分类法回顾和分类了这一主题中最相关的工作,并根据最近和新出现的威胁突出了开放的研究问题。因此,这项工作为CIDS的过去、现在和未来解决方案提供了很好的见解,并帮助研究人员和专业人员设计更有效的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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