Abstract

Smartphone applications have gained popularity in recent years due to the large footprint of mobile phone usage and availability of a large number of value-added applications. The official app stores (google, IOS, Microsoft, Amazon) provide a platform for hosting, publishing, distributing, and managing the mobile applications developed by companies and individuals. This mobile application ecosystem could be used to distribute the malicious apps which are specifically designed to track behavior of users, spy on the activities of users, and could be a threat to the privacy, confidentiality, and integrity of the users. In this paper, we present a novel approach called DEEPSEL (Deep Feature Selection), a deep learning-based method for the identification of malware and malicious codes within android applications. DEEPSEL uses a set of features to characterize the behavior of android applications and classify them as legitimate and malicious. The main contribution is characterized by the usage of particle swarm optimization for performing feature selection. We evaluated our approach on a public malware data-set which is composed of samples collected from 39 unique malware families. Our results show that the proposed method can achieve very good results with an accuracy of around 83.6% and an F-measure of around 82.5%.