Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi
Thanoon, Karam Hatim
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Static Analysis-based Detection of Android Malware using Machine Learning Algorithms Saied, Omar Emad; Thanoon, Karam Hatim
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5498

Abstract

The rapid growth of Android applications has led to increased security threats, making malware detection a critical concern in cybersecurity. This research proposes a static analysis-based technique that employs machine learning for Android malware detection. The proposed method utilizes three classification algorithms: Support Vector Machine (SVM), Random Forest, and Decision Tree. The tool extracts static permission features from APK files to evaluate their effectiveness. The dataset consists of 400 Android applications (200 benign and 200 malicious), which were analyzed using the three machine learning models. Their performance was evaluated and compared using accuracy , precision, recall, and F1-score. The Random Forest model achieved the highest accuracy. The results demonstrate that static analysis combined with a robust classification model can effectively identify malicious applications with a high degree of accuracy. Although the tool is reliable in detecting Android malware, it has limitations in handling obfuscated and dynamic threats. Future research could focus on integrating dynamic analysis techniques to improve detection accuracy and enhance resistance to evasion techniques
Co-Authors Saied, Omar Emad