RGB-based Android Malware Detection and Classification Using Convolutional Neural Network
Asim Darwaish, Farid Naït‐Abdesselam · 2020
With the proliferation of handheld devices and due to numerous routes for malware creation, the detection of new sophisticated malware becomes a real challenge. In contrary to conventional machine learning approaches, which require feature engineering and source code analysis, we propose here to use a new RGB-based imaging technique for android malware detection and classification. To combat malware threats, our system is built on a static analysis of the android application packaging (APK) file. First, we perform a novel transformation of the APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping. Second, we train a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The experimental results on the AndroZoo [1] dataset show that our system can classify both legacy and new malware applications with a high accuracy of 99.37%, a False Negative Rate (FNR) of 0.8%, and a False Positive Rate (FPR) of 0.39%.