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Applying Wide & Deep Learning Model for Android Malware Classification

Le Duc Thuan1,2,*, Pham Van Huong2, Hoang Van Hiep1, Nguyen Kim Khanh1

1 Ha Noi University of Science and Technology, Ha Noi, 100000, Viet Nam
2 Academy of Cryptography Techniques, Ha Noi, 100000, Viet Nam

* Corresponding Author: Le Duc Thuan. Email: email

Computer Systems Science and Engineering 2023, 45(3), 2741-2759. https://doi.org/10.32604/csse.2023.033420

Abstract

Android malware has exploded in popularity in recent years, due to the platform’s dominance of the mobile market. With the advancement of deep learning technology, numerous deep learning-based works have been proposed for the classification of Android malware. Deep learning technology is designed to handle a large amount of raw and continuous data, such as image content data. However, it is incompatible with discrete features, i.e., features gathered from multiple sources. Furthermore, if the feature set is already well-extracted and sparsely distributed, this technology is less effective than traditional machine learning. On the other hand, a wide learning model can expand the feature set to enhance the classification accuracy. To maximize the benefits of both methods, this study proposes combining the components of deep learning based on multi-branch CNNs (Convolutional Network Neural) with wide learning method. The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component. The proposed model, partition, and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features: API, permission, and raw image. The accuracy with Wide and Deep CNN (WDCNN) model is 98.64%, improved by 1.38% compared to RNN (Recurrent Neural Network) model.

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Cite This Article

L. D. Thuan, P. V. Huong, H. V. Hiep and N. K. Khanh, "Applying wide & deep learning model for android malware classification," Computer Systems Science and Engineering, vol. 45, no.3, pp. 2741–2759, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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