Android applications classification with deep neural networks

Abstract

Currently, Android is the most widely used mobile operating system globally. This platform has become a target for malware activities due to its technological and user appeal, open-source code, and the possibility of installing apps from third-party vendors without much restrictions or centralized control. Although it has security features, recent reports of malicious activities and Android’s vulnerabilities have heightened the need for robust frameworks and approaches to improve its security. Recent studies have proposed many security methods, using static analysis, dynamic analysis, and artificial intelligence techniques to prevent malware attacks. Current sophistication of Android malware infections has made the detection of malicious apps a significant challenge. In this study, deep-learning techniques for categorizing Android applications are examined. Initially, we suggested a deep belief neural network-based applications categorization approach. With clearly defined training and testing splits from the CIC-AAGM2017 Android datasets, we further trained and assessed our neural network’s classification performance against four conventional deep feed-forward neural networks and seven baseline models based on machine-learning algorithms. The experimental results showed that the proposed neural network could classify Android apps into benign and malicious categories with 98.7% accuracy. The classification accuracy of the DBN-based model is 1.86% higher than that of other deep learning-based models studied by recent research contributions.