Application encrypted traffic generation method and system based on generative adversarial network

Abstract

The invention discloses an application encrypted traffic generation method and system based on a generative adversarial network. An encrypted traffic packet (including a data packet header) of a realapplication is extracted into decimal data and intercepted to a fixed length (insufficient bits are supplemented by 0) and separated by comma, each row is one piece of traffic data, the traffic data is sent to a GAN (Generative Adversarial Network) for feature extraction, and after a generator and a discriminator of the GAN tend to be stable, a small amount of encrypted traffic of a real application is input into the generator of the GAN to generate any number of encrypted traffic containing the traffic characteristics of the application. According to the method, the features in the encryptedtraffic are ingeniously abstracted through the GAN, the traffic does not need to be decrypted, the decryption work is reduced, meanwhile, the privacy of a user is effectively protected, and the cost for obtaining a sample is greatly reduced. The method is suitable for all encrypted traffic recognition scenes based on deep learning, and the recognition rate is low due to the fact that encrypted traffic samples are difficult to obtain.

Description

technical field [0001] The invention relates to a method for generating application encryption traffic based on a generative confrontation network (GAN), and belongs to the technical field of data encryption. Background technique [0002] Traffic classification and identification are the basis for improving network management and security monitoring, improving service quality, and also the prerequisite for network behavior such as network design and planning. With the enhancement of user privacy protection and security awareness, technologies such as SSL, SSH, and VPN have been more and more widely used, resulting in an increasing proportion of encrypted traffic in network transmission. [0003] Due to the use of application layer encryption, traditional port matching and DPI can no longer accurately identify application traffic; compared with machine learning, deep learning can better express the essential characteristics of data, but its training relies on a large number o...

AI Technical Summary

Problems solved by technology

Because new data has the characteristics of application encryption, it can participate in the subsequent recognition model training, which greatly reduces the difficulty of obtaining deep learning samples and the high cost of marking

Images