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A Deep Convolutional Neural Network for Image Malware Classification

A Deep Convolutional Neural Network for Image Malware Classification

Mustapha Belaissaoui, József Jurassec
Copyright: © 2019 |Volume: 6 |Issue: 1 |Pages: 12
EISBN13: 9781522546795|ISSN: 2640-4079|EISSN: 2640-4087|DOI: 10.4018/ijsst.2019010104
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MLA

Belaissaoui, Mustapha, and József Jurassec. "A Deep Convolutional Neural Network for Image Malware Classification." IJSST vol.6, no.1 2019: pp.49-60. http://doi.org/10.4018/ijsst.2019010104

APA

Belaissaoui, M. & Jurassec, J. (2019). A Deep Convolutional Neural Network for Image Malware Classification. International Journal of Smart Security Technologies (IJSST), 6(1), 49-60. http://doi.org/10.4018/ijsst.2019010104

Chicago

Belaissaoui, Mustapha, and József Jurassec. "A Deep Convolutional Neural Network for Image Malware Classification," International Journal of Smart Security Technologies (IJSST) 6, no.1: 49-60. http://doi.org/10.4018/ijsst.2019010104

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Abstract

Malware classification and detection is an important factor in computer system security. However, signature-based methods currently used cannot provide an accurate detection of zero-day attacks and polymorphic viruses. This is why there is a need for detection based on machine learning. The purpose of this work is to present a deep neuronal classification method using convolutional and recurrent network layers in order to obtain the best features for classification. The proposed model achieves 98.73% accuracy on the Microsoft malware dataset.

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