Machine learning and deep learning approaches for cybersecurity: a review

• Main Authors: Halbouni, Asmaa Hani, Gunawan, Teddy Surya, Habaebi, Mohamed Hadi, Halbouni, Murad, Kartiwi, Mira, Ahmad, Robiah
• Format: Article
• Language: English; English
• Published: IEEE 2022
• Subjects: TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
• Online Access: http://irep.iium.edu.my/96736/7/96736_update.pdf; http://irep.iium.edu.my/96736/8/96736_scopus.pdf; http://irep.iium.edu.my/96736/; https://ieeexplore.ieee.org/document/9712274
• Institution: Universiti Islam Antarabangsa Malaysia

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.