Dr Zeeshan Ahmad z.ahmad1@rgu.ac.uk
Research Fellow
Network intrusion detection system: a systematic study of machine learning and deep learning approaches.
Ahmad, Zeeshan; Khan, Adnan Shahid; Wai Shiang, Cheah; Abdullah, Johari; Ahmad, Farhan
Authors
Adnan Shahid Khan
Cheah Wai Shiang
Johari Abdullah
Farhan Ahmad
Abstract
The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)-based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network-based IDS (NIDS) systems. A comprehensive review of the recent NIDS-based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL-based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL-based NIDS.
Citation
AHMAD, Z., KHAN, A.S., WAI SHIANG, C., ABDULLAH, J. and AHMAD, F. 2021. Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Transactions on emerging telecommunications technologies [online], 32(1), article number e4150. Available from: https://doi.org/10.1002/ett.4150
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 29, 2020 |
Online Publication Date | Oct 16, 2020 |
Publication Date | Jan 13, 2021 |
Deposit Date | Oct 1, 2024 |
Publicly Available Date | Oct 3, 2024 |
Journal | Transactions on emerging telecommunications technologies |
Electronic ISSN | 2161-3915 |
Publisher | Wiley |
Peer Reviewed | Peer Reviewed |
Volume | 32 |
Issue | 1 |
Article Number | e4150 |
DOI | https://doi.org/10.1002/ett.4150 |
Keywords | Deep learning; Machine learning; Network anomaly detection; Network intrusion detection system; Network security |
Public URL | https://rgu-repository.worktribe.com/output/2243587 |
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2020 The Authors. Transactions on Emerging Telecommunications Technologies published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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