Open Access

ARTICLE

Deep Convolution Neural Networks for Image-Based Android Malware Classification

Amel Ksibi^1,*, Mohammed Zakariah², Latifah Almuqren¹, Ala Saleh Alluhaidan¹

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 College of Computer and Information Sciences, King Saud University, Riyadh, P.O. Box 11442, Saudi Arabia

* Corresponding Author: Amel Ksibi. Email:

Computers, Materials & Continua 2025, 82(3), 4093-4116. https://doi.org/10.32604/cmc.2025.059615

Received 13 October 2024; Accepted 16 January 2025; Issue published 06 March 2025

Abstract

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a. Trojan, b. Adware, c. Ransomware, d. Spyware, e. Worm. These network traffic features are then converted to image formats for deep learning, which is applied in a CNN framework, including the VGG16 pre-trained model. In addition, our approach yielded high performance, yielding an accuracy of 0.92, accuracy of 99.1%, precision of 98.2%, recall of 99.5%, and F1 score of 98.7%. Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%. Through the results obtained, it is clear that CNNs are a very effective way to classify Android malware, providing greater accuracy than conventional techniques. The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

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Keywords

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