Malware Detection by Eating a Whole APK

Abstract

As Android is one of the most popular and widely used open-source mobile platforms, the security and privacy of Android apps are very critical, especially that over 6000 apps are added to the Google Play Store every day. This makes Android a prime target for malware. This paper proposes a modeling technique with experiments conducted using a dataset with about 10,000 benign and 10,000 malicious Android Application Packages (APK), in addition to other experiments that were conducted on the same dataset with a reduction in the number of benign files to be equal to 578 files. These files are analyzed using image classification techniques, where the whole APK file is converted into a grayscale image, and Convolutional Neural Networks (CNNs) with transfer-learning models are applied; to efficiently construct classification models for malware detection. Experiments have shown that the proposed technique has achieved favorable accuracy in the CNN model.