A Dynamic DL-Driven Architecture to Combat Sophisticated Android Malware

Abstract

The predominant Android operating system has captured enormous attention globally not only in smart phone industry but also for varied smart devices. The open architecture and application programming interfaces (APIs) while hosting third party applications has led to explosive growth of varied pervasive sophisticated Android malware production. In this study, we propose a robust, scalable and efficient Cuda-empowered multi-class malware detection technique leveraging Gated Recurrent Unit (GRU) to identify sophisticated Android malware. Experimentation of the proposed technique has been carried out using current state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Moreover, to rigorously evaluate the performance of the proposed technique, we have employed standard performance evaluation metrics (e.g., accuracy, precision, recall, F1-score etc.) and compared it with our constructed DL-driven architectures and benchmark algorithms. The GRU-based malware detection system outperforms with 98.99% detection accuracy for malware identification with a trivial trade off in speed efficiency.

Keywords

• Android malware
• deep learning
• recurrent neural network
• convolutional neural network
• deep neural network
• mobile security