Machine Learning and Deep Learning Approaches for CyberSecurity: A Review

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-fi...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.19572-19585
Main Authors: Halbouni, Asmaa, Gunawan, Teddy Surya, Habaebi, Mohamed Hadi, Halbouni, Murad, Kartiwi, Mira, Ahmad, Robiah
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Computer hacking
Computer security
Cybersecurity
Data integrity
Deep learning
Intrusion detection
intrusion detection system
Intrusion detection systems
Machine learning
Machine learning algorithms
Network security
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3151248