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Cuckoo-based Malware Dynamic Analysis

Volume 15, Number 3, March 2019, pp. 772-781
DOI: 10.23940/ijpe.19.03.p6.772781

Lele Wanga, Binqiang Wanga, Jiangang Liub, Qiguang Miaoc, and Jianhui Zhanga

aNational Digital Switching System Engineering and Technological Research Center, Zhengzhou, 450002, China

bNanjing Information Technology Institute, Nanjing, 210000, China

cDepartment of Computer Science, Xidian University, Xi’an, 710071, China


(Submitted on October 20, 2018; Revised on November 21, 2018; Accepted on December 23, 2018)

Abstract:

Aiming at the problems of the huge number of malware currently in the big data environment, the insufficient ability of automatic malware analysis available, and the inefficiency of the classification of malicious attributes, in this paper, we propose a Cuckoo-based malware dynamic analysis system that can be extended, analyzed quickly, and has application value. The system proposes a semantic feature model based on deep learning, uses a deep recursive neural network model to describe the multi-layered aggregation relationship of program semantics, and builds a malware semantic aggregation model. The model can automatically complete the acquisition and analysis of behavioural features of unknown program samples and perform attribute discrimination on unknown program samples efficiently and accurately.

 

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