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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika (JUTIF)
Ramadhani, Arsabilla
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Journal : Jurnal Teknik Informatika (JUTIF)

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IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING Ramadhani, Arsabilla; Rafrastara, Fauzi Adi; Rosyada, Salma; Ghozi, Wildanil; Osman, Waleed Mahgoub
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.3903

Abstract

Malware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99.1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.
Co-Authors Fauzi Adi Rafrastara Osman, Waleed Mahgoub Rosyada, Salma Wildanil Ghozi Yassin, Warusia