Automated adversarial detection in mobile apps using API calls and permissions

Abstract

Android mobile phones’ growing popularity has led to developers creating more malicious apps, which can be included in third-party arcades as protected applications. Detecting these malware applications is challenging due to time-consuming and high-cost techniques. This study proposes a robust deep learning (DL) model for detecting adversarial third-party apps using adaptive feature learning. The strategy involves preprocessing raw apk files, extracting permission behavioral features, and using the proposed spatial dropout-assisted convolutional autoencoder (SD_ConvAE) model to determine if the app is benign or malignant. The approach is simulated using a Python tool and assessed using various measures like accuracy, recall, weighted F-score (W-FS), false discovery rate (FDR), and kappa coefficient. The overall accuracies achieved by the developed techniques are about 99.6% and 99% for detecting benign and malignant apps, respectively.