Detecting Malware with an Ensemble Method Based on Deep Neural Network
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-12
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yan, Jinpei& Qi, Yong& Rao, Qifan. 2018. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1214332
Modern Language Association (MLA)
Yan, Jinpei…[et al.]. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1214332
American Medical Association (AMA)
Yan, Jinpei& Qi, Yong& Rao, Qifan. Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1214332
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214332