Abstract: An embodiment for root cause analysis of computerized system anomalies is provided. The embodiment may include monitoring key performance indicators (KPIs) for a computerized system, wherein KPI values of the monitored KPIs form respective timeseries. The embodiment may include detecting an anomaly in the computerized system based on the monitored KPIs. The embodiment may include determining a troubleshooting time window extending over a given time period. The embodiment may include identifying a strict subset of the monitored KPIs based on portions of the respective timeseries spanning the given time period. The strict subset comprises abnormal KPIs (aKPIs) and potential explanatory KPIs (xKPIs). The embodiment may include obtaining a causal graph of vertices mapping KPIs of the strict subset by running a causality algorithm to evaluate weights of directed edges connecting the vertices and accordingly obtain one or more directed paths. The embodiment may include returning the obtained causal graph.

Abstract: An embodiment for root cause analysis of computerized system anomalies is provided. The embodiment may include monitoring key performance indicators (KPIs) for a computerized system, wherein KPI values of the monitored KPIs form respective timeseries. The embodiment may include detecting an anomaly in the computerized system based on the monitored KPIs. The embodiment may include determining a troubleshooting time window extending over a given time period. The embodiment may include identifying a strict subset of the monitored KPIs based on portions of the respective timeseries spanning the given time period. The strict subset comprises abnormal KPIs (aKPIs) and potential explanatory KPIs (xKPIs). The embodiment may include obtaining a causal graph of vertices mapping KPIs of the strict subset by running a causality algorithm to evaluate weights of directed edges connecting the vertices and accordingly obtain one or more directed paths. The embodiment may include returning the obtained causal graph.

Abstract: The invention is directed to characterizing a computerized system based on key performance indicators (KPIs). Channel the KPIs through n buffer channels, where n?2. The KPIs are channeled by: accessing KPIs; clustering the KPIs accessed to obtain k clusters; and, for each cluster of the k clusters obtained, identifying n representative KPIs in each cluster as objects of n respective types and buffering KPI values of the n representative KPIs identified in respective ones of the n buffer channels. The KPI values buffered in the n buffer channels are then fed into respective input channels of a trained neural network. The neural network independently processes the KPIs in the input channels and produces output values in output of the inner layers. Eventually, the computerized system is characterized based on the output values produced, e.g., to detect an anomaly in the system.

Abstract: The invention is directed to characterizing a computerized system. Access key performance indicators (KPIs), for the computerized system. Each of the KPIs is a timeseries of KPI values and is categorized into one of n types. KPI values are channeled through n buffer channels. Each buffer channel buffers KPI values of one of n types. Finally, reconstructions errors are obtained by feeding initial KPI values to n respective input channels of a cognitive model, implemented as an autoencoder by a trained neural network including an encoder and a decoder. Encoder has temporal convolutional layer blocks connected by each input channel. Decoder has deconvolution layer blocks connected by encoder. Initial KPI values are independently processed in n input channels, then compressed by encoder, prior to being reconstructed by decoder. Reconstruction errors are obtained by comparing reconstructed KPI values with initial KPI values. Computerized system is characterized based on reconstruction errors obtained.

Abstract: Computer-implemented methods and systems are provided for digitally signing predetermined arrays of digital data. Such a method may provide a secret neural network model trained to classify arrays of digital data in dependence on data content of the arrays. The array of the arrays may be signed by supplying the array to the secret neural network model to obtain an initial classification result; and effecting a modification of data in the array to change the initial classification result to a predetermined, secret classification result, the modification being effected via a backpropagation process in the secret neural network model to progressively modify the array in response to backpropagated errors dependent on a difference between a current classification result for the array and the secret classification result.

Abstract: Computer-implemented methods and systems are provided for digitally signing predetermined arrays of digital data. Such a method may provide a secret neural network model trained to classify arrays of digital data in dependence on data content of the arrays. The array of the arrays may be signed by supplying the array to the secret neural network model to obtain an initial classification result; and effecting a modification of data in the array to change the initial classification result to a predetermined, secret classification result, the modification being effected via a backpropagation process in the secret neural network model to progressively modify the array in response to backpropagated errors dependent on a difference between a current classification result for the array and the secret classification result.