Analysis on intrusion detection system using machine learning techniques

Abstract

Intrusion detection system [IDS] is a significant base for the network defence. A huge amount of data is generated with the latest technologies like cloud computing and social media networks. As the data generation keeps increasing, there are chances that different forms of intrusion attacks are also possible. This paper mainly focuses on the machine learning (ML) techniques for cyber security in support of intrusion detection. It uses three different algorithms, namely Naïve Bayes classifier, Hoeffding tree classifier and ensemble classifier. The study is performed on emerging methods and is compared with streaming and non-streaming environment. The discussion on using the emerging methods and challenges is presented in this paper with the well-known NSL_KDD datasets. The concept of drift is induced in the static stream by using the SEA generator. Finally, it is found that the ensemble classifier is more suitable for both the environments with and without concept drift.