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- Title
Cuckoo-based Malware Dynamic Analysis.
- Authors
Lele Wang; Binqiang Wang; Jiangang Liu; Qiguang Miao; Jianhui Zhang
- 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.
- Subjects
MALWARE; DEEP learning; ARTIFICIAL neural networks; COMPUTER software; FEATURE extraction
- Publication
International Journal of Performability Engineering, 2019, Vol 15, Issue 3, p772
- ISSN
0973-1318
- Publication type
Article
- DOI
10.23940/ijpe.19.03.p6.772781