Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Jan Lansky, Saqib Ali, Mokhtar Mohammadi, Mohammed Kamal Majeed, Sarkhel H. Taher Karim, Shima Rashidi, Mehdi Hosseinzadeh, Amir Masoud Rahmani
2021 IEEE Access  
This work was supported by the University of Finance and Administration through the institutional support for long-term conceptual development of research of "System approach to selected information and communications technology trends" under Standard Project 7429/2020/02. ABSTRACT Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations' data
more » ... and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted. INDEX TERMS Intrusion detection, auto-encoder, recurrent neural network, Boltzmann machine, CNN.
doi:10.1109/access.2021.3097247 fatcat:un54rxgjyvfx3pxberzpafioxm