Adaptive Android APKs Reverse Engineering for Features Processing in Machine Learning Malware Detection

Benjamin Aruwa Gyunka (1) , Aro Taye Oladele (2) , Ojeniyi Adegoke (3)
(1) Department of Branch Operations Central Bank of Nigeria, Kano, Nigeria
(2) Department of Mathematical and Computing Sciences, KolaDaisi University, Ibadan, Nigeria
(3) Department of Computer Science, Maldives National University, Male, Maldives
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The key component that makes the detection of android malware possible is the availability of the right triggers and pointers, which are found in the Android application packages, known as features or attributes. These are fundamental in the training of the different machine learning algorithms to produce the required detection model. The process of extracting these attributes or features, from the Android application packages, is known as reverse engineering. This paper delved into the experimental detail processes of applying reverse engineering procedure, using Sublime Text 2 and Androguard Plugin, on Android Application packages for the extraction of, particularly permissions, which are the targeted features. The study further discussed the cleaning stages, using NotePad++, Microsoft Excel Worksheet, and MS Word, to sort out all the relevant and important features by removing all the noisy ones. A total of 1500 Android apps were downloaded from both benign and malicious sources and used for the experiment. The cleaned or important features extracted from these application packages at the end of the reverse engineering processes are 162 in total and these were further used to form a feature binary matrix of size 1500 by 163 (including the class features).

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How to Cite
B. A. Gyunka, A. T. Oladele, and O. Adegoke, “Adaptive Android APKs Reverse Engineering for Features Processing in Machine Learning Malware Detection”, Int. J. Data. Science., vol. 4, no. 1, pp. 10-25, May 2023.


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