Method for extracting voiceprint feature vectors of encrypted network flow

Abstract

The invention provides a method for extracting voiceprint feature vectors of encrypted network flow. The method includes steps of S2, creating a voiceprint feature vector extraction sliding window for the encrypted network flow; S3, updating the voiceprint feature extraction sliding window by the aid of current data packets; S5, filtering the voiceprint feature extraction sliding window; S7, screening unit groups containing more k units from the sliding window; S9, sequentially extracting voiceprint features of each unit group. The method has the advantages that dynamic features of the network flow can be effectively described by the extracted voiceprint features; merits of the traditional method for extracting statistical features of network flow are inherited, rich and reliable knowledge can be provided, and technical support can be provided for designing and implementing high-performance flow classification systems in high-speed networks.

Description

technical field [0001] The invention belongs to the technical field of flow classification in network technology, and in particular relates to a method for extracting a feature vector of encrypted network flow voiceprints. Background technique [0002] The rapid development of network technology and network bandwidth has posed new challenges to network security: First, the data traffic in the network has doubled, and network data arrives at high speed and without interruption, presenting the characteristics of massive data, which cannot be stored locally. Therefore, the efficiency of the traditional traffic classification technology based on data packet load cannot meet the needs; secondly, the services carried by the network are becoming more and more abundant, and more and more network services are encapsulated and transmitted using encryption protocols. The wide use of encryption protocols makes The traditional traffic classification technology based on packet load cannot...

AI Technical Summary

Problems solved by technology

[0014] 2. No other data packets arrive within 15 seconds after receiving a data packet;

Using the existing statistical feature extraction method, although the traffic classification technology based on network flow can effectively identify the encrypted proxy channel protocol, it cannot identify the hidden protocol in the channel

[0020] To sum up, because the statistical feature extraction method adopted by the current classification technology can only extract static statistical features from the network flow, the traffic classification system cannot correctly classify the traffic of the private encryption protocol, let alone effectively identify the traffic hidden in the encrypted proxy channel. protocol

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