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A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Aiyanyo, Imatitikua D. Samuel, Hamman Lim, Heuiseok |
| Copyright Year | 2020 |
| Description | This is a systematic review of over one hundred research papers about machine learning methods applied to defensive and offensive cybersecurity. In contrast to previous reviews, which focused on several fragments of research topics in this area, this paper systematically and comprehensively combines domain knowledge into a single review. Ultimately, this paper seeks to provide a base for researchers that wish to delve into the field of machine learning for cybersecurity. Our findings identify the frequently used machine learning methods within supervised, unsupervised, and semi-supervised machine learning, the most useful data sets for evaluating intrusion detection methods within supervised learning, and methods from machine learning that have shown promise in tackling various threats in defensive and offensive cybersecurity. |
| Starting Page | 5811 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10175811 |
| Journal | Applied Sciences |
| Issue Number | 17 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-08-22 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Cybernetical Science Cybersecurity Machine Learning Artificial Intelligence Data Mining Defensive Security Offensive Security Intrusion Detection Systems |
| Content Type | Text |
| Resource Type | Article |
