Cyber Threat Prediction with Machine Learning

Publication Type:

Journal Article


Information & Security: An International Journal, Volume 47, Issue 2, p.203-220 (2020)


auto-encoding, clustering with outliers, Cybersecurity, DBSCAN, deep learning, KNIME Analytics Platform, machine learning, MITRE ATT@CK framework


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.

Last updated: Tuesday, 20 October 2020