Telefonica, S.a.

Sandra Gómez Canaval

Antonio Agustin Pastor Perales

Diego R Lopez

Alberto Mozo Velasco

Edgar Talavera Muñoz

Ángel González Prieto

Diego R LÓPEZ

A method for modelling a shape of data in a GAN comprising a generator agent (G) for generating synthetic data () and a discriminator agent (D) for distinguishing between the generated synthetic data () and real original data () that follow an arbitrary, continuous or discrete, distribution defined by a n-dimensional vector of input variables x. The method, before generating output synthetic data (), computes an Inverse Smirnov transformation ffor each of the n input variables xand attaches an activation function () to the generator agent (G), wherein the activation function () is a n-dimensional vector fformed by the computed Inverse Smirnov transformations, f=(f, f, . . . , f). The GAN implementing the method generates the output synthetic data () using the activation function () which outputs synthetic data () with a distribution (') whose shape is the same as the arbitraty distribution of the original data ().

Method for modelling synthetic data in generative adversarial networks

A system and method for training and validating ML algorithms in real networks, including: generating synthetic traffic and receiving it along with real traffic; aggregating the received traffic into network flows by using metadata and transforming them to generate a first dataset readable by the ML algorithm, comprising features defined by the metadata; labelling the traffic and selecting a subset of the features from the labelled dataset used in an iterative training to generate a trained model; filtering out a part of real traffic to obtain a second labelled dataset; and selecting a subset of features from the second labelled dataset used for validating the trained model by comparing predicted results for the trained model and the labels; repeating the steps with a different subset of features to generate another trained model until results are positive in terms of precision or accuracy.

Method and system for training and validating machine learning in network environments

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Madrid, Spain

Sandra Gómez Canaval