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http://dx.doi.org/10.9728/dcs.2018.19.6.1177

The Malware Detection Using Deep Learning based R-CNN  

Cho, Young-Bok (Department of Computer & Information Security, Daejeon University)
Publication Information
Journal of Digital Contents Society / v.19, no.6, 2018 , pp. 1177-1183 More about this Journal
Abstract
Recent developments in machine learning have attracted a lot of attention for techniques such as machine learning and deep learning that implement artificial intelligence. In this paper, binary malicious code using deep learning based R-CNN is imaged and the feature is extracted from the image to classify the family. In this paper, two steps are used in deep learning to image malicious code using CNN. And classify the characteristics of the family of malicious codes using R-CNN. Generate malicious code as an image, extract features, classify the family, and automatically classify the evolution of malicious code. The detection rate of the proposed method is 93.4% and the accuracy is 98.6%. In addition, the CNN processing speed for image processing of malicious code is 23.3 ms, and the R-CNN processing speed is 4ms to classify one sample.
Keywords
Malware; Deep learning; Regions with CNN(R-CNN); Image Processing;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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