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.

Abstract: A method for modelling a shape of data in a GAN comprising a generator agent (G) for generating synthetic data (110, 220) and a discriminator agent (D) for distinguishing between the generated synthetic data (120, 220) and real original data (110) that follow an arbitrary, continuous or discrete, distribution defined by a n-dimensional vector of input variables xi. The method, before generating output synthetic data (220), computes an Inverse Smirnov transformation fSi?1 for each of the n input variables xi and attaches an activation function (200) to the generator agent (G), wherein the activation function (200) is a n-dimensional vector faG formed by the computed Inverse Smirnov transformations, faG=(fS1?1, fS2?1, . . . , fSn?1). The GAN implementing the method generates the output synthetic data (220) using the activation function (200) which outputs synthetic data (220) with a distribution (420, 420?) whose shape is the same as the arbitraty distribution of the original data (110).

Abstract: 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.

Abstract: A system and method for validating proof of transit of network traffic through network nodes (N), the node (N) comprising a set of input interfaces (20) receiving incoming packets, a first module (A) to identify a matching route within a routing table (23) and storing means (22) to provide next modules (B, C, D) with two private keys if the packet is matched and/or the packet metadata includes OPoT information. The second module (B) decrypts the OPoT metadata using the first private key associated to the link of the node from which the incoming packets are received. The node (N) has SSS metadata to be processed by a third module (C) for the correct generation of cumulative validation parameters. When the SSS process is finished by the third module (C), the fourth module (D) re-encrypts the OPoT metadata using the second private key before packet forwarding to the subsequent node in the path through output interfaces (21).

Abstract: A system and method for optimizing event prediction in data systems, wherein at least one source (100) comprises: a data collector periodically collecting (101) real data values (300) to generate a stream of data modeled as a time series; a generator (110) of prediction models (M1, M2, M3, . . . , Mx) to which the collected values from the data collector are input; a first forecast module (120) receiving (102) one of the generated prediction models (M1, M2, M3, . . . , Mx) for generating a predicted value (310) and computing a committed error (320) by comparing the predicted value (310) with the real data value (300); and wherein the source (100) sends (105) the committed error (320) within the time series to the destination (200) only if the committed error (320) exceeds a threshold and wherein the destination (200) comprises: a second forecast module (210) receiving (204) the same prediction model (M1, M2, M3, . . .

Abstract: 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.

Abstract: 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.

Abstract: A system and method for validating proof of transit of network traffic through network nodes (N), the node (N) comprising a set of input interfaces (20) receiving incoming packets, a first module (A) to identify a matching route within a routing table (23) and storing means (22) to provide next modules (B, C, D) with two private keys if the packet is matched and/or the packet metadata includes OPoT information. The second module (B) decrypts the OPoT metadata using the first private key associated to the link of the node from which the incoming packets are received. The node (N) has SSS metadata to be processed by a third module (C) for the correct generation of cumulative validation parameters. When the SSS process is finished by the third module (C), the fourth module (D) re-encrypts the OPoT metadata using the second private key before packet forwarding to the subsequent node in the path through output interfaces (21).

Abstract: A network controller and a method for automatically define forwarding rules to configure a computer networking device, The network controller (100) is connected to a sub-network (A) of a communication network and comprises: a controller manager (101) 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 (120a) in order to configure it for said given service; a deciding module (102) configured to communicate with the controller manager (101) and configured to interact with a DNS server (150) to receive a determined resolution for a DNS request of said request for said given service, and with a database (300) to retrieve information supplementary for the DNS request, in order to assist the controller manager (101) in performing the defining of the forwarding rules; and a plurality of interfaces (SA, SB, SD) for allowing the communication between the different elements.

Abstract: 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.

Abstract: A network controller and a method for automatically define forwarding rules to configure a computer networking device, The network controller (100) is connected to a sub-network (A) of a communication network and comprises: a controller manager (101) 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 (120a) in order to configure it for said given service; a deciding module (102) configured to communicate with the controller manager (101) and configured to interact with a DNS server (150) to receive a determined resolution for a DNS request of said request for said given service, and with a database (300) to retrieve information supplementary for the DNS request, in order to assist the controller manager (101) in performing the defining of the forwarding rules; and a plurality of interfaces (SA, SB, SD) for allowing the communication between the different elements.

Abstract: The method comprising receiving, an ALTO server at a network layer, a request from an ALTO client at an application layer to obtain network cost for a connection; computing, the cost information concerning PIDs stored in the ALTO server and adapting, by a request controller, the computed cost information to an ALTO information further sending it to the ALTO server and the latter to said ALTO client; receiving, by a provisioning server, a request from a provisioning client to compute a path for setting up a connection between said PIDs and transmitting said request to a provisioning controller; mapping, the provisioning controller, said received request from the provisioning server into network addresses and computing said path according to said network addresses; and comparing, a network cost of the computed path with a cost information previously stored by the request controller in a request database.