Network encryption traffic classification method and system based on multi-feature learning

Abstract

The invention belongs to the technical field of network security, and particularly relates to a network encryption traffic classification method and system based on multi-feature learning, and the method comprises the steps: carrying out the preprocessing of an original traffic data set, and obtaining a traffic data package vector used for the input of a deep learning model; respectively inputting the traffic data packet vectors into a trained multi-channel CNN model and a trained LSTM model for parallel learning, extracting the data packet space features through the multi-channel CNN model, and extracting traffic time sequence features through the LSTM model; carrying out vector splicing on the data packet space feature and the traffic time sequence feature to obtain an omnibearing traffic feature vector; and inputting the omni-directional traffic feature vector into a neural network full-connection layer, and obtaining an encrypted traffic classification type through a traffic type probability. According to the method, the traffic features can be comprehensively and automatically extracted and utilized from the angles of the spatial features and the time features, the classification capability of the encrypted traffic is improved, and the method has good application value.

Description

technical field [0001] The invention belongs to the technical field of network security, in particular to a method and system for classifying network encrypted traffic based on multi-feature learning. Background technique [0002] In recent years, due to the continuous development of encryption technology, traffic encryption technology has been widely used on the Internet. Encryption technology not only protects the privacy and anonymity of ordinary Internet users, but also enables users to bypass the detection of firewalls and monitoring systems, which provides opportunities for malicious users, for example, attackers encrypt malware communications , to anonymously invade and attack the system, etc. It can be said that the abuse of encryption technology has brought new threats to network security and network management. Therefore, the problem of identification and classification of encrypted traffic classification has attracted extensive attention from academia and indust...

AI Technical Summary

Problems solved by technology

Although this can save computing and storage overhead, it will inevitably affect the classification performance of the model

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