A Review of current machine learning approaches for
anomaly detection in network traffic
Ali, Wasim A.ORCID: 0000-0002-4602-461X, Manasa, K.N., Bendechache, MalikaORCID: 0000-0003-0069-1860, Fadhel Aljunaid, MohammedORCID: 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
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.