Intrusion detection using machine learning methods

Abstract

Nowadays, with intruders frequently attempting to gain access to the network and harm the data, intrusion detection systems (IDS) are playing a more and more important role in providing security against malicious activities, making IDS a popular research issue. Among various intrusion detection techniques, machine learning methods have shown their advantages, such as higher detection rates, lower false alarm rates and reasonable computation and communication costs. In this paper, we describe a focused literature review of mainstream machine learning methods for intrusion detection. We divide the schemes into three categories, including classic supervised machine learning methods, ensemble learning methods and deep learning methods, and several algorithms are discussed with respect to each category.