Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

Hong Zhang; Rui Hou; Lei Yi; Juan Meng; Zhisong Pan; Yuhuan Zhou

doi:10.1117/12.2242369

11 July 2016 Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

Hong Zhang, Rui Hou, Lei Yi, Juan Meng, Zhisong Pan, Yuhuan Zhou

Author Affiliations +

Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100111G (2016) https://doi.org/10.1117/12.2242369
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan

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.

Citation Download Citation

Hong Zhang, Rui Hou, Lei Yi, Juan Meng, Zhisong Pan, and Yuhuan Zhou "Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111G (11 July 2016); https://doi.org/10.1117/12.2242369

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