A Review of Machine Learning Methodologies for Network Intrusion Detection

Abstract

The Internet continues to spread itself over the globe, providing a great opportunity for various threats which are growing on a daily basis. Current static detection techniques only detect known malicious attacks and they also require frequent updates to signature-based databases. To reduce this work, systems are proposed for network intrusion detection systems capable of analyzing contents of the network by means of machine learning techniques to analyze and classify the malicious contents. Various machine learning algorithms are used for developing a Network Intrusion Detection System. The review intends to provide an exhaustive survey of the currently proposed machine learning based intrusion detection systems in order to assist Network Intrusion Detection System developers to gain a better intuition.