Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction
Dublin Core
Title
Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction
Subject
malware; machine learning; ensemble algorithm; important features
Description
By 2022 it is estimated that 29 billion devices have been connected to the internet so that cybercrime will become a major
threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end
users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system
(Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has
infected a user's device. This study uses the opposite approach, which is to predict the potential for malware infection on the
user's device before the infection occurs. Similar studies still use single algorithms, while this study uses ensemble algorithms
that are more resistant to bias-variance trade-off. This study builds models from data on computer features that affect the
possibility of malware infection on computer devices with Microsoft Windows operating system using ensemble algoritms, such
as Bagging Classifier, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Machine, Category
Boosting, and Stacking Classifier. The best model is Stacking Classifier, which is a combination of Light Gradient Boosting
Machine and Category Boosting Classifier, with training and test results of 0.70665 and 0.64694. Important features have also
been identified as a reference for taking policies to protect user devices from malware infections.
threat. One of the most common forms of cybercrime is infection with malicious software (malware) designed to harm end
users. Microsoft has the highest number of vulnerabilities among software companies, with the Microsoft operating system
(Windows) contributing to the largest vulnerabilities at 68.85%. Malware infection research is mostly done when malware has
infected a user's device. This study uses the opposite approach, which is to predict the potential for malware infection on the
user's device before the infection occurs. Similar studies still use single algorithms, while this study uses ensemble algorithms
that are more resistant to bias-variance trade-off. This study builds models from data on computer features that affect the
possibility of malware infection on computer devices with Microsoft Windows operating system using ensemble algoritms, such
as Bagging Classifier, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting Machine, Category
Boosting, and Stacking Classifier. The best model is Stacking Classifier, which is a combination of Light Gradient Boosting
Machine and Category Boosting Classifier, with training and test results of 0.70665 and 0.64694. Important features have also
been identified as a reference for taking policies to protect user devices from malware infections.
Creator
Yusuf Bayu Wicaksono, Christina Juliane
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
June 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Yusuf Bayu Wicaksono, Christina Juliane, “Comparative Analysis of Various Ensemble Algorithms for Computer Malware Prediction,” Repository Horizon University Indonesia, accessed February 4, 2026, https://repository.horizon.ac.id/items/show/10005.