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http://dx.doi.org/10.13089/JKIISC.2016.26.1.197

Visualized Malware Classification Based-on Convolutional Neural Network  

Seok, Seonhee (Pusan National University)
Kim, Howon (Pusan National University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.26, no.1, 2016 , pp. 197-208 More about this Journal
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;
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Times Cited By KSCI : 2  (Citation Analysis)
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