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Security and Communication Networks
Volume 2018 (2018), Article ID 7247095, 16 pages
Research Article

Detecting Malware with an Ensemble Method Based on Deep Neural Network

Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Correspondence should be addressed to Yong Qi

Received 18 August 2017; Revised 3 December 2017; Accepted 6 February 2018; Published 12 March 2018

Academic Editor: Zonghua Zhang

Copyright © 2018 Jinpei Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Malware detection plays a crucial role in computer security. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset.