Identification of Encrypted Data Stream Based on Sparse Randomness Features and GMM

Abstract

It is important to identify encrypted data stream on the network. The accurate identification of encrypted data stream not only helps insight into the operation of the entire network, but also accurately controls user behavior for specific requirements. In this paper, we proposed Gaussian mixture model using sparse feature selection of randomness to solve the identification of encrypted data stream. Experimental results show that the average identification rate of encrypted data stream is over 90%.