Telefonica, S.a.

Telefonica Digital Espana, S.l.u.

Diego R Lopez

Antonio Agustin Pastor Perales

Alberto Mozo Velasco

Sandra Gómez Canaval

Pablo Rodriguez Rodriguez

Konstantina Papagiannaki

Jeremy Blackburn

Ángel González Prieto

Matteo Varvello

Peter Steenkiste

Pedro A Aranda Gutiérrez

David Naylor

Ilias Leontiadis

Norisy C Orea Barrios

Edgar Talavera Muñoz

Kyle Schomp

Luis Miguel Contreras

Antonio Agustin Pastor

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

A method, a system and computer program products for securely enabling in-network functionality over encrypted data sessions, the method involving establishing an encrypted data session between a client communication application () and a server communication application () over a communication network; receiving and/or transmitting, by the client communication application (), in the established encrypted data session, at least one encrypted communication data (D) from/to the server communication application () through a computing network element (M); and performing, by the computing network element (M), different actions other than data packet forwarding from one communication application to the other on the encrypted communication data (D). The encrypted communication data (D) has a plurality of data portions, or contexts, (CTX), each encrypted by a context key, and the different actions being specific for the computing network element (M) and for one or more of the contexts (CTX_X).

Method, a system and computer program products for securely enabling in-network functionality over encrypted data sessions

The method comprising, in a network based on a chain of individual Service Functions, SFs, that are composed to implement Network Services, NSs: assigning, at an ingress node of a network architecture, to at least one data packet received by said ingress node from the network, a unique cryptographic tag; processing said assigned unique cryptographic tag using a cryptographic function specific to each Service Function, SF; and verifying, at a given point of the network architecture, said processed unique cryptographic tag by applying a cryptographic verification function composed by the inverse functions of the cryptographic functions associated to the SFs traversed by the at least one data packet.

Method to assure correct data packet traversal through a particular path of a network

A network controller and a method for automatically define forwarding rules to configure a computer networking device, The network controller () is connected to a sub-network (A) of a communication network and comprises: a controller manager () that receives a request for a given service, defines forwarding rules related to said service and installs the defined forwarding rules into a computer networking device () in order to configure it for said given service; a deciding module () configured to communicate with the controller manager () and configured to interact with a DNS server () to receive a determined resolution for a DNS request of said request for said given service, and with a database () to retrieve information supplementary for the DNS request, in order to assist the controller manager () in performing the defining of the forwarding rules; and a plurality of interfaces (S, S, S) for allowing the communication between the different elements.

Network controller and a computer implemented method for automatically define forwarding rules to configure a computer networking device

Growing community of inventors

Madrid, Spain

Diego R Lopez