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Handling minority class problem in threats detection based on heterogeneous ensemble learning approach.

Eke, Hope; Petrovski, Andrei; Ahriz, Hatem

Authors

Hatem Ahriz



Abstract

Multiclass problem, 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 of time. 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 our 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.

Citation

EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Handling minority class problem in threats detection based on heterogeneous ensemble learning approach. International journal of systems and software security and protection [online], 13(3), pages 13-37. Available from: https://doi.org/10.4018/IJSSSP.2020070102

Journal Article Type Article
Acceptance Date Mar 6, 2020
Online Publication Date Jul 31, 2020
Publication Date Dec 31, 2020
Deposit Date Jun 1, 2020
Publicly Available Date Aug 1, 2021
Journal International journal of systems and software security and protection
Print ISSN 2640-4265
Electronic ISSN 2640-4273
Publisher IGI Global
Peer Reviewed Peer Reviewed
Volume 11
Issue 2
Article Number 2
Pages 13-37
DOI https://doi.org/10.4018/IJSSSP.2020070102
Keywords Imbalance data; Resampling techniques; Multi-steps; Multiclass classification; Oversampling; SMOTE; Recurrent neural network; Long short-term memory; Gated recurrent unit; Ensemble techniques
Public URL https://rgu-repository.worktribe.com/output/877439

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