Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Kamal Majeed, Mohammed (2021) Deep Learning-Based Intrusion Detection Systems: A Systematic Review. IEEE Access, 9. pp. 101574-101599.

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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.

Item Type: Article
Uncontrolled Keywords: Intrusion detection, auto-encoder, recurrent neural network, Boltzmann machine, CNN.
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: ePrints deposit
Date Deposited: 23 Sep 2021 07:35
Last Modified: 23 Sep 2021 07:35
URI: http://eprints.tiu.edu.iq/id/eprint/734

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