Cyber Threat Prediction with Machine Learning
Source:
Information & Security: An International Journal,Volume: 47,
Issue2,
p.203-220
(2020)
Keywords:
auto-encoding, clustering with outliers, Cybersecurity, DBSCAN, deep learning, KNIME Analytics Platform, machine learning, MITRE ATT@CK frameworkAbstract:
In this paper we address the approaches, techniques and results of applying machine learning techniques for cyber threat prediction. Timely discovery of advanced persistent threats is of utmost importance for the protection of NATO’s and its allies’ networks. Therefore, NATO and NATO Communication and Information Agency’s Cyber Security service line is constantly looking for improvements. During Coalition Warrior Interoperability Exercise (CWIX) event data was captured on a Red-Blue Team Simulation. The data set was then used to apply a variety of Machine Learning techniques: deep-learning, auto-encoding and clustering with outliers.
138
Views
525
Downloads
Journal of the Korea Academia-Industrial cooperation Society, Academic journal
25,
no. 9
(2024):
262 - 269 .
College Of Business, Innovation, Leadership, and Technology
Doctor of Science
(2024):
2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
(2023):
2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)
(2023):
7th ICITB - Conference on Information Technology and Business 2021
(2021):