Author(s):
Journal
MDPI AG
Abstract
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.
Concluding remarks
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.
Reference details
DOI
10.3390/app10175811
Resource type
Journal Article
Year of Publication
2020
ISSN Number
2076-3417
Publication Area
Dual-use cybersecurity
Date Published
2020-08-22
How to cite this reference:
Aiyanyo, I. D., Samuel, H., & Lim, H. (2020). A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning. MDPI AG. https://doi.org/10.3390/app10175811 (Original work published)