Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Zeeshan, Ahmad and Adnan Shahid, Khan and Cheah Wai, Shiang and Johari, Abdullah and Farhan, Ahmad (2021) Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32 (1). pp. 1-29. ISSN 2161-3915

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Abstract

The rapid advances in the internet and communication fields have resulted in ahuge increase in the network size and the corresponding data. As a result, manynovel attacks are being generated and have posed challenges for network secu-rity to accurately detect intrusions. Furthermore, the presence of the intruderswiththeaimtolaunchvariousattackswithinthenetworkcannotbeignored.Anintrusion detection system (IDS) is one such tool that prevents the network frompossible intrusions by inspecting the network traffic, to ensure its confidential-ity, integrity, and availability. Despite enormous efforts by the researchers, IDSstillfaceschallengesinimprovingdetectionaccuracywhilereducingfalsealarmrates and in detecting novel intrusions. Recently, machine learning (ML) anddeep learning (DL)-based IDS systems are being deployed as potential solutionsto detect intrusions across the network in an efficient manner. This article firstclarifiestheconceptofIDSandthenprovidesthetaxonomybasedonthenotableML and DL techniques adopted in designing network-based IDS (NIDS) sys-tems. A comprehensive review of the recent NIDS-based articles is provided bydiscussing the strengths and limitations of the proposed solutions. Then, recenttrends and advancements of ML and DL-based NIDS are provided in terms ofthe proposed methodology, evaluation metrics, and dataset selection. Using theshortcomings of the proposed methods, we highlighted various research chal-lenges and provided the future scope for the research in improving ML andDL-based NIDS

Item Type: Article
Uncontrolled Keywords: Deep learning, Machine learning, Network anomaly detection, Network intrusion detection system, Network security
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Khan
Date Deposited: 15 Feb 2022 01:08
Last Modified: 15 Feb 2022 01:08
URI: http://ir.unimas.my/id/eprint/37907

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