Application of Machine Learning Algorithms in Network Intrusion Detection

Abstract

With the rapid development of computer network technology, cyberspace security has received more and more attention. As an important means of ensuring cyberspace security, intrusion detection technology has also developed rapidly in recent years. Machine learning is improving the efficiency of the intrusion detection and reducing the false positive. This paper is mainly divided into three parts. The first part briefly introduces the application process and common data sets of machine learning methods in the field of intrusion detection, and conducts an in-depth analysis of the application of traditional machine learning algorithms in intrusion detection. The second part expounds the specific application of deep learning as a subfield of machine learning in intrusion detection. In this part, according to the classification of generative model, recognition models and hybrid models, it is found that the intrusion detection system based on deep learning generally has a higher detection rate, which can make up for the defects of machine learning algorithm. The third part summarizes the full text and makes an outlook on the future development direction of intrusion detection technology.