A Systematic Review of Various Deep Learning Techniques for Network Intrusion Detection System

The collective frequency and intricacy of attacks on computer networks remain a threat to information security within computer schemes. In order to solve this, researchers exploited network intrusion detection systems (NIDSs) to safe guard networks and information. Because of its lively nature of malware and continuous change in attacks, these systems normally recognize intrusions by the examination of network traffic. This survey demonstrates several Deep Learning (DL) approaches for the identification and categorization of unexpected attacks. The constant variations in the activities of the network make it essential to examine several datasets by dynamic and static techniques. Here 25 research papers are analyzed and surveyed. A complete assessment of many DL classifiers was exposed on various benchmark malware datasets. First, it offers a classification of computer intrusions with an explanation of categorized methods. Next, a general outline of the NIDS is described with its basic features. Then, the classification of NIDS based on their categorized methods is explained. After that, the challenges faced by the categorized methods are deliberated in the section of research gaps and issues. Lastly, the analysis in the survey is performed in terms of publication year, category analysis, tools used and performance metrics.