Machine Learning Techniques for Network Anomaly Detection: A Survey
Sohaila Eltanbouly, May Bashendy, Noora Al‐Naimi, Zina Chkirbene, Aiman Erbad
Abstract
Nowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit and experiment with different types of attacks. Therefore, many intrusion detection techniques have been evolving to protect cloud distributed services by detecting the different attack types on the network. Machine learning techniques have been heavily applied in intrusion detection systems with different algorithms. This paper surveys recent research advances linked to machine learning techniques. We review some representative algorithms and discuss their proprieties in detail. We compare them in terms of intrusion accuracy and detection rate using different data sets.