Machine learning and deep learning approaches for cybersecurity: a review

Halbouni, Asmaa Hani and Gunawan, Teddy Surya and Habaebi, Mohamed Hadi and Halbouni, Murad and Kartiwi, Mira and Ahmad, Robiah (2022) Machine learning and deep learning approaches for cybersecurity: a review. IEEE ACCESS, 10. pp. 19572-19585. ISSN 2169-3536

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Abstract

The rapid evolution and growth of the internet through the last decades led to more concern about cyber-attacks that are continuously increasing and changing. As a result, an effective intrusion detection system was required to protect data, and the discovery of artificial intelligence’s sub-fields, machine learning, and deep learning, was one of the most successful ways to address this problem. This paper reviewed intrusion detection systems and discussed what types of learning algorithms machine learning and deep learning are using to protect data from malicious behavior. It discusses recent machine learning and deep learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection system.

Item Type:	Article (Journal)
Additional Information:	5588/96736
Uncontrolled Keywords:	Cybersecurity, Machine Learning, Deep Learning, Intrusion Detection System.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	14 Feb 2022 19:03
Last Modified:	01 Mar 2022 10:14
URI:	http://irep.iium.edu.my/id/eprint/96736

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