Computer-implemented method for accelerating convergence in the training of generative adversarial networks (gan) to generate synthetic network traffic, and computer programs of same

Abstract

Proposed are a computer-implemented method for accelerating convergence in the training of generative adversarial networks (GAN) to generate synthetic network traffic, and computer programs of same. The method allows the GAN network to ensure that the training converges in a limited time period less than the standard training period of existing GAN networks. The method allows results to be obtained in different use scenarios related to the generation and processing of network traffic data according to objectives such as the creations of arbitrary amounts of simulated data (a) with characteristics (statistics) similar to real datasets obtained from real network traffic, but (b) without including any part of any real dataset; diversity in the type of data to be created: IP traffic, network attacks, etc.; and the detection of changes in the network traffic patterns analysed and generated.