Overview of Deep Learning Techniques for Network Intrusion Detection Systems

Abstract

The rapid advances in the new digital world are producing vast amounts of data. This gives more opportunities in business management, but it can also help in implementing new security techniques. Intrusion detection systems (IDS) are enforcing processes for analyzing network data. This study is reviewing the main Deep Learning approaches for intrusion detection in IT network traffic. In the beginning, the study gives an overview of the various IDS types and their usability in the IT network. Then it presents some of the most used Deep Learning techniques proposed by the research community in recent years. By analyzing various papers on the subject, current achievements, and limitations in developing IDS are detected and presented. The study ends by providing the future reach of the newly proposed Deep Learning techniques in monitoring and detecting malicious activities in network traffic.