TY - JOUR
AU - Imatitikua D. Aiyanyo
AU - Hamman Samuel
AU - Heuiseok Lim
AB - 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.
BT - MDPI AG
DA - 2020-08-22
DO - 10.3390/app10175811
N1 - 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.
N2 - 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.
PY - 2020
T2 - MDPI AG
TI - A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
UR - https://www.mdpi.com/2076-3417/10/17/5811
SN - 2076-3417
ER -