Abstract
Over the past decade, mobile devices have become an integral part of our daily lives. These devices rely on applications to deliver a diverse range of services and functionalities to users, such as social networks or online shopping apps. The usage of these applications has led to the emergence of novel security risks, facilitating the rapid proliferation of malicious apps. To deal with the increasing numbers of Android malware in the wild, deep learning models have emerged as promising detection systems. In this paper, we propose an Android malware detection system using Convolutional Neural Networks (CNN). To accomplish this objective, we trained three distinct models (VGG16, RESNET50, and InceptionV3) on the image representation of the Dalvik executable format. Our assessment, conducted on a dataset of more than 13000 samples, showed that all three models performed up to 99% of the detection of malicious Android applications. Finally, we discuss the potential benefits of employing this type of solution for detecting Android malware.
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Miranda-Garcia, A., Pastor-López, I., Urquijo, B.S., de la Puerta, J.G., Bringas, P.G. (2023). Bytecode-Based Android Malware Detection Applying Convolutional Neural Networks. In: García Bringas, P., et al. International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). CISIS ICEUTE 2023 2023. Lecture Notes in Networks and Systems, vol 748. Springer, Cham. https://doi.org/10.1007/978-3-031-42519-6_11
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