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Visualized Malware Classification Based-on Convolutional Neural Network
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 Title & Authors
Visualized Malware Classification Based-on Convolutional Neural Network
Seok, Seonhee; Kim, Howon;
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 Abstract
In this paper, we propose a method based on a convolutional neural network which is one of the deep neural network. So, we convert a malware code to malware image and train the convolutional neural network. In experiment with classify 9-families, the proposed method records a 96.2%, 98.7% of top-1, 2 error rate. And our model can classify 27 families with 82.9%, 89% of top-1,2 error rate.
 Keywords
Malware Classification;Malware Image;Convolutional Neural Network;
 Language
Korean
 Cited by
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