A Systematic Review on Network Intrusion Detection System based on machine learning and deep learning approach

¹ Research Scholar, Department of Electrical and Electronics Engineering, PSR Engineering College, Sivakasi, Tamilnadu
² Professor, Department of Electrical and Electronics Engineering, PSR Engineering College, Sivakasi, Tamilnadu .

^* Corresponding author email id- kgpunitha@gmail.com

Abstract

Today’s security attacks on computer networks are becoming more complex and severe, which has prompted security researchers to use a variety of machine learning techniques to safeguard the information and reputation of their clients. Detecting network infiltration has long been a difficult task. Machine learning advancements have raised the way for improving intrusion detection systems (IDS). This development has led to intrusion detection’s integration into network security. Using supervised machine learning techniques, intrusion detection has attained great detection accuracy. However, it is unlikely that a machine learning (ML) classifier will be able to correctly identify all attacks, particularly obscure ones.An approach based on deep learning is presented for more precise intrusion detection. This review article presents an extensive survey and classification of deep learning-based intrusion detection techniques with an emphasis on these approaches. The main background ideas about the IDS architecture and several machine and deep learning approaches are initially presented. Then, it categorizes these schemes based on the many types of methodologies each one employs. It explains how accurate intrusion detection is achieved through the use of machine and deep learning networks. The researched IDS frameworks are then fully analysed, with final thoughts and suggested directions for the future underlined.