Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection

International Journal On Cyber Situational Awareness (IJCSA)

ISSN: (Print) 2057-2182 ISSN: (Online) 2633-495X

DOI: 10.22619/IJCSA

Published Semi-annually. Est. 2014


Dr Cyril Onwubiko, Director – Artificial Intelligence, Blockchain & Cyber Security, Research Series, London, UK; IEEE DVP

Associate Editors:

Professor Frank Wang, Professor of Future Computing, Chair IEEE Computer Society, UK&RI, School of Computing, University of Kent, Canterbury, UK

Professor Karen Renaud, Professor of Cyber Security, University of Strathclyde, Glasgow, Scotland, UK

Evaluation of Selected Stacked Ensemble Models for the Optimal Multi-class Cyber-Attacks Detection

Olasehinde Olayemi Oladimeji, Alese Boniface Kayode, Adetunmbi Adebayo Olusola & Aladesote Olomi Isaiah


The significant rise in the frequency and sophistication of cyber-attacks and their diversity necessitated various researchers to develop strong and effective approaches to address recurring cyber threat challenges. This study evaluated the performance of three selected meta-learning models for optimal multi-class detection of cyber-attacks using the University of New South Wales 2015 Network benchmark (UNSW-NB15) Intrusion Dataset. The results of this study show and confirm the ability of the three base models; Naive Bayes, C4.5 Decision Tree, and K-Nearest Neighbor for solving multi-class problems. It further affirms the knack of the duo of feature selection techniques and stacked ensemble learning to optimize ML models’ performances. The stacking of the predictions of the information gain base models with Model Decision Tree meta-algorithm recorded the most improved and optimal cyber-attacks detection accuracy and Mattew’s correlation Coefficient than the stacking with the Multiple Model Trees (MMT) and Multi Response Linear regression (MLR) Meta algorithms.

Keywords: Cyber-attacks, Base model, stacked ensemble, Meta Learners, Evaluation, performance improvement, Intrusion, Feature selection.

ISSN: (Print) 2057-2182 (Online) 2633-495X

Volume 5. No. 1

DOI: 10.22619/IJCSA.2020.100132

Date: Jan. 2021

Reference to this paper should be made as follows: Olasehinde O.O., Alese B. K., Adetunmbi A. O. & Aladesote O. I. (2020). Evaluation of Selected Stacked Ensemble Models for Optimal Multi-class Attacks Detection. International Journal on Cyber Situational Awareness, Vol. 5, No. 1, pp26-48.

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