A Convolutional Neural Network Based Classifier for Uncompressed Malware Samples
| Title | A Convolutional Neural Network Based Classifier for Uncompressed Malware Samples |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Yang, Chun, Wen, Yu, Guo, Jianbin, Song, Haitao, Li, Linfeng, Che, Haoyang, Meng, Dan |
| Conference Name | Proceedings of the 1st Workshop on Security-Oriented Designs of Computer Architectures and Processors |
| Date Published | January 2018 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5991-7 |
| Keywords | Artificial neural networks, Collaboration, convolutional neural networks (cnns), cyber physical systems, deep learning (dl), malware classification, Metrics, policy-based governance, pubcrawl, Resiliency, tensorflow framework, uncompressed gray-scale images |
| 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% |
| URL | https://dl.acm.org/doi/10.1145/3267494.3267496 |
| DOI | 10.1145/3267494.3267496 |
| Citation Key | yang_convolutional_2018 |