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
