Toward Mobile Malware Detection Through Convolutional Neural Networks

Abstract

Traditional research on mobile malware detection has focused on approaches that rely on analyzing bytecode for uncovering malicious apps. Unfortunately, cybercriminals can bypass such methods by embedding malware directly in native machine code, making traditional methods inadequate. Another challenge that detection solutions face is scalability. The sheer number of malware released every year makes it difficult for solutions to efficiently scale their coverage. This letter presents an energy efficient solution that uses convolutional neural networks (CNNs) to defend against malware. We show that systematically converting native instructions from Android apps into images using Hilbert space-filling curves and entropy visualization techniques enable CNNs to reliably detect malicious apps with near ideal accuracy. We characterize popular CNN architectures that have been known to perform well on different computer vision tasks and evaluate their effectiveness against malware using an Android malware dataset.