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Author/Affiliation: Daniel Koranek
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Open AccessArticle
7 Pages, 262 KB Download PDF

Detecting Malicious Assembly using Convolutional, Recurrent Neural Networks

Advances in Science, Technology and Engineering Systems Journal, Volume 4, Issue 5, Page # 46–52, 2019; DOI: 10.25046/aj040506
Abstract:

We present findings on classifying the class of executable code using convolutional, re- current neural networks by creating images from only the .text section of executables and dividing them into standard-size windows, using minimal preprocessing. We achieve up to 98.24% testing accuracy on classifying 9 types of malware, and 99.50% testing accuracy on classifying malicious…

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(This article belongs to the Special Issue on Advancement in Engineering and Computer Science 2019 & Section Interdisciplinary Applications of Computer Science (CSI))

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