Deep learning-driven methods for network-based intrusion detection systems: A systematic review

IF 4.1 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ICT Express Pub Date : 2025-02-01 DOI:10.1016/j.icte.2025.01.005

Ramya Chinnasamy , Malliga Subramanian , Sathishkumar Veerappampalayam Easwaramoorthy , Jaehyuk Cho

引用次数: 0

Abstract

This paper presents a systematic review of deep learning (DL) techniques for Network-based Intrusion Detection Systems (NIDS) based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses: (PRISMA2020) guidelines. It explores recent advancements in data preparation, DL architectures, and performance evaluation metrics for NIDS. The review provides insights into various datasets and tools used in the field, highlighting the effectiveness of DL in improving NIDS performance. Additionally, it discusses the applications of NIDS across different industries and identifies emerging research trends, offering a comprehensive resource for researchers and practitioners in cybersecurity.

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来源期刊

ICT Express Multiple-

CiteScore

10.20

自引率

1.90%

发文量

167

审稿时长

35 weeks

期刊介绍： The ICT Express journal published by the Korean Institute of Communications and Information Sciences (KICS) is an international, peer-reviewed research publication covering all aspects of information and communication technology. The journal aims to publish research that helps advance the theoretical and practical understanding of ICT convergence, platform technologies, communication networks, and device technologies. The technology advancement in information and communication technology (ICT) sector enables portable devices to be always connected while supporting high data rate, resulting in the recent popularity of smartphones that have a considerable impact in economic and social development.