A review on machine learning methods for intrusion detection system

Abstract

With the increasing access to the Internet and the development of information technology, concerns about computer security have been raised on a considerably large scale. Computer crimes contain various methods to undermine information privacy and system integrity, causing millions to trillions lose in the past few years. It is urgent to improve the security algorithms and models to perform as a thorough structure to prevent attacks. Among this prevention structure, an intrusion detection system (IDS) has played a vital role to monitor and detect malicious behaviours. However, due to the rapidly increasing variety of threats, the traditional algorithms are not sufficient, and new methods should be brought into IDS to improve the functionality. Deep learning (DL) and Machine learning (ML) are newly developed programs which can process data on a considerably large scale. They can also make decisions and predictions without specific programming, and these features are suitable to improve and enhance the IDS. This article mainly focuses on a review of ML methods used in IDS construction.