Supervised Learning Methods Comparison for Android Malware Detection Based on System Calls Referring to ARM (32-bit/EABI) Table

DOI: https://doi.org/10.30996/jitcs.10511
Keywords: Android, Decision Tree, Machine Learning, Malware Detection, Supervised Learning, Naive Bayes

Abstract

Android malware detection research is a topic that is still being developed. From all the detection techniques developed, dynamic analysis methods have become interesting because they trace the suspect application system calls. Based on the system calls, by utilizing machine learning, the suspect application can be classified as malware or benign. Comparing the machine learning methods is im-portant to determine what method is best to support malware detection. This article aims to explain more clearly and simply the way to conduct Android malware detection based on system calls step by step using classification. Furthermore, it presents the system calls sequence conversion referring to the arm(32-bit/EABI) table, which has 398 system calls (0-397) as features. It will provide a compari-son of several supervised machine-learning methods for classifying Android applications. This initial research is part of the other research that has the purpose of developing a malware detection system based on an Android application. This research can be used to develop the best machine learning to classify malware applications using a Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes (NB). The result can be concluded that the KNN method has the lowest performance in detecting Android malware apps, with an accuracy of only 0.50. In comparison, the NB method has an accuracy of only 0,69. SVM and DT models have similar accuracy and recall results of 0.79 and 0.75, respectively, but DT obtained higher precision and scores of 0.83 and 0.76, respectively. Although in this study, the classification performance of DT is better than SVM, based on comparison with the results of previous research, SVM is a suitable method for Android malware de-tection based on system calls. It is proven by the results of research comparisons that the SVM method is always the method with the highest accuracy score among other methods. For the next research, the SVM method can be used to develop a malware detection system for Android applications.

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Author Biographies

Rinanza Zulmy Alhamri, Politeknik Negeri Malang

Department of Information Technology

Toga Aldila Cinderatama, Politeknik Negeri Malang

Department of Information Technology

Kunti Eliyen, Politeknik Negeri Malang

Department of Information Technology

Abidatul Izzah, Politeknik Negeri Malang

Department of Information Technology

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Published
2024-06-20
How to Cite
Alhamri, R. Z., Cinderatama, T. A., Eliyen, K., & Izzah, A. (2024). Supervised Learning Methods Comparison for Android Malware Detection Based on System Calls Referring to ARM (32-bit/EABI) Table. Journal of Information Technology and Cyber Security, 2(1), 15-24. https://doi.org/10.30996/jitcs.10511
Issue
Vol 2 No 1 (2024): January
Section
Research Article

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