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Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks

Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks

Brahami Menaouer, Abdallah El Hadj Mohamed Islem, Matta Nada
Copyright: © 2023 |Volume: 19 |Issue: 1 |Pages: 22
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781668479063|DOI: 10.4018/IJIIT.329956
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MLA

Menaouer, Brahami, et al. "Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks." IJIIT vol.19, no.1 2023: pp.1-22. http://doi.org/10.4018/IJIIT.329956

APA

Menaouer, B., Islem, A. E., & Nada, M. (2023). Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks. International Journal of Intelligent Information Technologies (IJIIT), 19(1), 1-22. http://doi.org/10.4018/IJIIT.329956

Chicago

Menaouer, Brahami, Abdallah El Hadj Mohamed Islem, and Matta Nada. "Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural Networks," International Journal of Intelligent Information Technologies (IJIIT) 19, no.1: 1-22. http://doi.org/10.4018/IJIIT.329956

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Abstract

In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98.50%. Other performance measures used in the study are precision, recall, and F1-score. Finally, the accuracy and results of these techniques are analyzed by comparing the effectiveness with previous works.