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- W2895873723 abstract "This paper proposes a deep learning based method for efficient malware classification. Specially, we convert the malware classification problem into the image classification problem, which can be addressed through leveraging convolutional neural networks (CNNs). For many malware families, the images belonging to the same family have similar contours and textures, so we convert the Binary files of malware samples to uncompressed gray-scale images which possess complete information of the original malware without artificial feature extraction. We then design classifier based on Tensorflow framework of Google by combining the deep learning (DL) and malware detection technology. Experimental results show that the uncompressed gray-scale images of the malware are relatively easy to distinguish and the CNN based classifier can achieve a high success rate of 98.2%" @default.
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- W2895873723 date "2018-01-15" @default.
- W2895873723 modified "2023-10-16" @default.
- W2895873723 title "A Convolutional Neural Network based Classifier for Uncompressed Malware Samples" @default.
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- W2895873723 doi "https://doi.org/10.1145/3267494.3267496" @default.
- W2895873723 hasPublicationYear "2018" @default.
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