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Ahmadi, M., Ulyanov, D., Semenov, S., Trofimov, M. and Giacinto, G. (2016) Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification. Cornell University Library. arXiv:1511.04317

has been cited by the following article:

• TITLE: Malware Images Classification Using Convolutional Neural Network

  AUTHORS: Espoir K. Kabanga, Chang Hoon Kim

  KEYWORDS: Malware, Convolutional Neural Network, Malware Classification

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, December 29, 2017

  ABSTRACT: Deep learning has been recently achieving a great performance for malware classification task. Several research studies such as that of converting malware into gray-scale images have helped to improve the task of classification in the sense that it is easier to use an image as input to a model that uses Deep Learning’s Convolutional Neural Network. In this paper, we propose a Con-volutional Neural Network model for malware image classification that is able to reach 98% accuracy.