A Review on Intrusion Detection System using Machine Learning Techniques

Abstract

In the present world, the protection of networks in the computing environment is one of the most difficult and essential challenges to cyber-security. Intrusion Detection (ID) is a significant key mechanism to provide computer networks with security. Intrusion Detection System (IDS) is a popular system for intrusion detection which is impending through an Internet. Securing networks become a substantial issue to provide services through a network with an increment of growing dependence and attacks on different fields such as finance, medicine, entertainment and engineering. The major aim of IDS is to detect malicious actions in network traffic or particular computer environment analysis and take necessary actions. This study analyses and reviews the research scenario of IDSs based on Machine Learning (ML) techniques into a comprehensible taxonomy and recognizes the gaps in this critical research area. This article provides complete details about the advantages and disadvantages of all the mentioned approaches. A comparative analysis is presented among the approaches based on their working methodology. This study also concentrated on the latest developments in the datasets of IDS which are used by different communities of researchers to develop effective and efficient ML technique-based IDS. The major aim of this work is to provide a comprehensive and strong comparative study of latest research on review spam detection by using different ML techniques and to develop a methodology for directing the investigation to next level.