将生成式机器学习应用于入侵检测：系统映射研究与回顾

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-04-20 DOI:10.1145/3659575

James Halvorsen, Clemente Izurieta, Haipeng Cai, Assefaw H. Gebremedhin

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引用次数: 0

摘要

入侵检测系统（IDS）是现代网络防御的重要组成部分，可在网络攻击发生的时间和地点向用户发出警报。机器学习可以使 IDS 进一步区分良性和恶意行为，但它也面临着一些挑战，包括缺乏高质量的训练数据和高误报率。生成式机器学习模型（GMLM）有助于克服这些挑战。本文深入探讨了 GMLM 在入侵检测中的应用。它给出了：(1) 对 GMLM 和 IDS 交叉领域研究的系统性映射研究；(2) 提供见解和未来研究方向的详细综述。

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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Applying Generative Machine Learning to Intrusion Detection: A Systematic Mapping Study and Review

Intrusion Detection Systems (IDSs) are an essential element of modern cyber defense, alerting users to when and where cyber-attacks occur. Machine learning can enable IDSs to further distinguish between benign and malicious behaviors, but it comes with several challenges, including lack of quality training data and high false positive rates. Generative Machine Learning Models (GMLMs) can help overcome these challenges. This paper offers an in-depth exploration of GMLMs’ application to intrusion detection. It gives: (1) a systematic mapping study of research at the intersection of GMLMs and IDSs, and (2) a detailed review providing insights and directions for future research.

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.