A Low Computational Cost Method for Mobile Malware Detection Using Transfer Learning and Familial Classification Using Topic Modelling

Abstract

With the extensive use of Android applications, malware growth has been increasing drastically. The high popularity of Android devices has motivated malware developers to attack these devices. In recent times, most researchers and scholars have used deep learning approaches to detect Android malware. Although deep learning techniques provide good accuracy and efficiency, they require high computational cost to train huge and complex data sets. Hence, there is a need for an approach that can efficiently detect novel malware variants with a minimum computational cost. This paper proposes a novel framework for detecting and clustering Android malware using the transfer learning and the topic modelling approach. The transfer learning approach minimizes new training data by transferring well-known features from a qualified source model to a destination model, and hence, a high amount of computational power is not required. In addition, the proposed framework clusters the detected malware variants into their corresponding families with the help of Latent Dirichlet Allocation and hierarchical clustering techniques. For performance assessment, we performed several experiments with more than 50K Android application samples. In addition, we compared the performance of our framework with that of similar existing traditional machine learning and deep learning models. The proposed framework provides better accuracy of 98.3% during the classification stage by using the transfer learning approach as compared to other state-of-the-art Android malware detection techniques. The high precision value of 98.7% is obtained during the clustering stage while grouping the obtained malicious applications into their corresponding malware families.