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 Convolutional Neural Network model for malware image classification that is able to reach 98% accuracy.
In the same vein, REF apply the convolutional neural network to the classification of malware image.
Malware Images Classification Using Convolutional Neural Network
Journal of Computational Chemistry
Chemistry