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Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification
Joint Authors
Huang, Cheng
Guo, Hui
Huang, Shuguang
Shi, Fan
Zhang, Min
Pan, Zulie
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-04
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Information Technology and Computer Science
Abstract EN
In recent years, the research on malware variant classification has attracted much more attention.
However, there are still many challenges, including the low accuracy of classification of samples of similar malware families, high time, and resource consumption.
This paper proposes a new method of malware classification based on multiple visual features of malware and deep learning algorithms.
In prior research, visualization techniques and entropy demonstrated exemplary performance in many areas.
This paper extracts numerous visual features from the raw bytes and entropy sequence of the malware, which makes it more sensitive to malware samples of similar families and endows it the ability to classify malware variants more accurately.
To evaluate the proposed method, this paper conducted a series of experiments on two malware datasets with a total of more than 20,000 samples provided by the Malware Research Lab and Microsoft Research.
Through experiments, the method showed its superiority compared with some leading malware visual classification methods, achieving good performance on the accuracy with at least 1% improvement.
The accuracy of the method even could reach 99.73% and 99.54%, respectively, on the two datasets.
American Psychological Association (APA)
Guo, Hui& Huang, Shuguang& Huang, Cheng& Shi, Fan& Zhang, Min& Pan, Zulie. 2020. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
Modern Language Association (MLA)
Guo, Hui…[et al.]. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks No. 2020 (2020), pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
American Medical Association (AMA)
Guo, Hui& Huang, Shuguang& Huang, Cheng& Shi, Fan& Zhang, Min& Pan, Zulie. Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-19.
https://search.emarefa.net/detail/BIM-1208849
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208849