[This article belongs to Volume - 54, Issue - 02]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-26-07-2022-263

Title : Deep Learning-Based Network Intrusion Detection: An Anatomization
Divya Nehra, Veenu Mangat, Krishan Kumar

Abstract :

Network security is being increasingly breached with more ethereal intrusion methods, broadening the challenge of providing integrity and security to networks. During the past few years, substantial research has been conducted for fabricating new methods to thwart various network attacks. This review article scrutinizes those research contributions with the help of a lucid systematic literature review(SLR) process. The sources used for data retrieval are Web of Science, Science Direct, ACM digital library and the IEEE Xplore (Institute of Electrical and Electronics Engineers). A total of 64 crucial studies publicized from the year 2017 to thus far were carefully chosen. The history of the network intrusion evolution is specified for a better understanding of the need for intrusion detection. A comparative study of datasets used in various research studies is presented in order to evaluate the suitability of the dataset. The SLR is applied with the goal of discovering the contemporary trends in the detection of network intrusion. This review offers a comprehensive resource background for researchers interested in NIDSs. This review also discusses various challenges that need attention and has recommendations for probable upcoming research tendencies.