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 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: Methods and apparatus are disclosed for controlling and distributing data traffic among neighboring access networks within a geographic area. Subscribers may be given different priorities. Requests of services by subscribers may be prioritized. Data traffic within a congested access network may be offloaded to alternate access networks based on criteria such as service types and Quality of Service requirements associated with the data traffic, subscriber priorities associated with the subscribers, software and hardware capabilities associated with the user terminals of the subscribers, and resource availabilities in the alternate access networks.

Abstract: Methods and apparatus are disclosed for controlling and distributing data traffic among neighboring access networks within a geographic area. Subscribers may be given different priorities. Requests of services by subscribers may be prioritized. Data traffic within a congested access network may be offloaded to alternate access networks based on criteria such as service types and Quality of Service requirements associated with the data traffic, subscriber priorities associated with the subscribers, software and hardware capabilities associated with the user terminals of the subscribers, and resource availabilities in the alternate access networks.