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Koulou, Younes
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Android malware detection using the random forest algorithm El Attaoui, Anas; El Hami, Norelislam; Koulou, Younes
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1876-1883

Abstract

The rapid growth in Android device usage has resulted in a significant increase in malware targeting this platform, posing serious threats to user security and privacy. This research tackles the challenge of Android malware detection by leveraging advanced machine learning techniques, with a particular emphasis on the random forest (RF) algorithm. Our primary objective is to accurately identify and classify malicious applications to enhance the security of Android devices. In this study, we employed the RF algorithm to analyze a comprehensive dataset of Android applications, where the classification of each application as either malware or benign is known. The method was rigorously tested, yielding impressive results: an average accuracy of 98.47%, a sensitivity of 98.60%, and an F-score of 98.60%. These metrics underscore the effectiveness of our approach. Moreover, we conducted a comparative analysis of the RF algorithm against other malware detection methods. The results demonstrate that the RF algorithm outperforms these alternative methods, offering superior detection capabilities and contributing to more robust Android security measures.
Co-Authors El Attaoui, Anas El Hami, Norelislam