TY  - EJOU
AU  - Ksibi, Amel 
AU  - Zakariah, Mohammed 
AU  - Almuqren, Latifah 
AU  - Alluhaidan, Ala Saleh 

TI  - Deep Convolution Neural Networks for Image-Based Android Malware Classification
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - 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.
KW  - Android malware detection; deep convolutional neural network (DCNN); image processing; CIC-AndMal2017 dataset; exploratory data analysis; VGG16 model

DO  - 10.32604/cmc.2025.059615