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*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

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 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.

Original languageEnglish
Article number9483916
Pages (from-to)101574-101599
Number of pages26
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • auto-encoder
  • Boltzmann machine
  • CNN
  • Intrusion detection
  • recurrent neural network

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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