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-field...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
IEEE
2022
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| Subjects: | |
| 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 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| 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. |
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