A Novel Image-Based Malware Classification Model Using Deep Learning

Yongkang Jiang, Shenghong Li, Yue Wu, Futai Zou

Abstract: Nowadays, the vast volume of data which needs to be evaluated potentially malicious is becoming one of the major challenges of antivirus products. In this paper, we propose a novel image-based malware classification model using deep learning to counter large-scale malware analysis. The model includes a malware embedding method called YongImage which maps instruction-level information and disassembly metadata generated by IDA disassembler tool into an image vector, and a deep neural network named malVecNet which has simpler structure and faster convergence rate.