MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization. (1st April 2019)

• Record Type: Journal Article
• Authors: Zhao, Yuntao; Xu, Chunyu; Bo, Bo; Feng, Yongxin
• Other Names: Wei Jin Guest Editor.
• Is Part Of: Security and communication networks. Volume 2019(2019)
• Publication Date: 2019-04-01
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2019/4895984
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.