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Proceedings Paper

Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model
Author(s): Hong Zhang; Rui Hou; Lei Yi; Juan Meng; Zhisong Pan; Yuhuan Zhou
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Paper Abstract

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.

Paper Details

Date Published: 11 July 2016
PDF: 12 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111G (11 July 2016); doi: 10.1117/12.2242369
Show Author Affiliations
Hong Zhang, National Univ. of Defense Technology (China)
Rui Hou, The Institute of China Electronic System Engineering Co. (China)
Lei Yi, PLA Univ. of Science and Technology (China)
Juan Meng, PLA Univ. of Science and Technology (China)
Zhisong Pan, PLA Univ. of Science and Technology (China)
Yuhuan Zhou, PLA Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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