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A Review of current machine learning approaches for anomaly detection in network traffic

Ali, Wasim A. orcid logoORCID: 0000-0002-4602-461X, Manasa, K.N., Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860, Fadhel Aljunaid, Mohammed orcid logoORCID: 0000-0001-9099-3664 and Sandhya, P. (2020) A Review of current machine learning approaches for anomaly detection in network traffic. Journal of Telecommunications and the Digital Economy, 8 (4). pp. 64-95. ISSN 2203-1693

Abstract
Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies in the last five years have been investigated to explore modern techniques with future opportunities. In this regard, the related literature on anomaly detection systems in network traffic has been discussed, with a variety of typical applications such as WSNs, IoT, high-performance computing, industrial control systems (ICS), and software-defined network (SDN) environments. Finally, we underlined diverse open issues to improve the detection of anomaly systems.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Anomaly Detection; Intrusion, Networks; Supervised; Unsupervised
Subjects:Computer Science > Artificial intelligence
Computer Science > Computer security
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Lero: The Irish Software Engineering Research Centre
Research Institutes and Centres > ADAPT
Publisher:Telecommunication Society of Australia Ltd.
Official URL:https://doi.org/10.18080/jtde.v8n4.307
Copyright Information:© 2020 Telecommunications Association Inc. (CC-BY-ND 4.0)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Malika Bendechache is supported, in part, by Science Foundation Ireland (13/RC/2094 and 13/RC/2106)
ID Code:25222
Deposited On:18 Jan 2021 16:09 by Malika Bendechache . Last Modified 18 Jan 2021 16:09
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