Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges

Al‐Bander, Baidaa and Maseer, Ziadoon K. and Kadhim, Qusay Kanaan and Yusof, Robiah and Saif, Abdu (2024) Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges. IET Networks, 13 (5-6). pp. 339-376. ISSN 2047-4954

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Abstract

Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta‐analysis to provide an in‐depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS‐based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security.

Item Type:	Article
Uncontrolled Keywords:	Computer network security, Computer networks
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	05 Feb 2025 16:14
Last Modified:	05 Feb 2025 16:14
URI:	http://eprints.utem.edu.my/id/eprint/28389
Statistic Details:	View Download Statistic

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