Extraction Method of Encrypted Network Flow Voiceprint Feature Vector

Abstract

The present invention provides a method for extracting voiceprint feature vectors from encrypted network streams, comprising the steps of: S2. establishing a voiceprint feature vector extraction sliding window for encrypted network streams; S3. updating the voiceprint feature vector extraction sliding window using current data packets; S5. Filtering the voiceprint feature vector to extract the sliding window; S7. Screening out unit groups with more than k units in a row from the sliding window; S9. Extracting the voiceprint features of each unit group in sequence. The voiceprint features extracted by the invention can effectively describe the dynamic features of the network flow. The method of the present invention not only inherits the advantages of the traditional network flow statistical feature extraction method, but also can provide more abundant and reliable knowledge, and can provide technical support for the design and realization of a high-performance traffic classification system in a high-speed network.

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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