A survey of network intrusion detection systems based on deep learning approaches
https://doi.org/10.17586/2226-1494-2023-23-2-352-363
Abstract
Currently, most IT organizations are inclined towards a cloud computing environment because of its distributed and scalable nature. However, its flexible and open architecture is receiving lots of attention from potential intruders for cyber threats. Here, Intrusion Detection System (IDS) plays a significant role in monitoring malicious activities in cloud-based systems. The state of the art of this paper is to systematically review the existing methods for detecting intrusions based upon various techniques, such as data mining, machine learning, and deep learning methods. Recently, deep learning techniques have gained momentum in the intrusion detection domain, and several IDS approaches are provided in the literature using various deep learning techniques to deal with privacy concerns and security threats. For this purpose, the article focuses on the deep IDS approaches and investigates how deep learning networks are employed by different approaches in various steps of the intrusion detection process to achieve better results. Then, it provided a comparison of the deep learning approaches and the shallow machine learning methods. Also, it describes datasets that are most used in IDS.
About the Authors
D. Al-Safaar
College of Science for Women; University of Babylon
Iraq
Iraq
Duaa Wahab Al-Safaar — Magister, Lecturer
Babylon, 51002
W. Al-Yaseen
Karbala Technical Institute; Al-Furat Al-Awsat Technical University
Iraq
Iraq
Wathiq Laftah Al-Yaseen — Associate Professor, D.Sc., Head of
Computer Center
Karbala, 56001
sc 57188754655
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For citations:
Al-Safaar D., Al-Yaseen W. A survey of network intrusion detection systems based on deep learning approaches. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2023;23(2):352-363. https://doi.org/10.17586/2226-1494-2023-23-2-352-363
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