Handling Minority Class Problem in Threats Detection Based on Heterogeneous Ensemble Learning Approach

Abstract

Multiclass problems, such as detecting multi-steps behaviour of advanced persistent threats (APTs), have been a major global challenge due to their capability to navigates around defenses and to evade detection for a prolonged period. Targeted APT attacks present an increasing concern for both cyber security and business continuity. Detecting the rare attack is a classification problem with data imbalance. This paper explores the applications of data resampling techniques together with heterogeneous ensemble approach for dealing with data imbalance caused by unevenly distributed data elements among classes with the focus on capturing the rare attack. It has been shown that the suggested algorithms provide not only detection capability but can also classify malicious data traffic corresponding to rare APT attacks.