DISCOVERING CRITICAL ALERTS THROUGH LEARNING OVER HETEROGENEOUS TEMPORAL GRAPHS

    公开(公告)号:WO2018093807A1

    公开(公告)日:2018-05-24

    申请号:PCT/US2017/061664

    申请日:2017-11-15

    Abstract: A method is provided that includes transforming training data into a neural network based learning model using a set of temporal graphs derived from the training data. The method includes performing model learning on the learning model by automatically adjusting learning model parameters based on the set of the temporal graphs to minimize differences between a predetermined ground-truth ranking list and a learning model output ranking list. The method includes transforming testing data into a neural network based inference model using another set of temporal graphs derived from the testing data. The method includes performing model inference by applying the inference and learning models to test data to extract context features for alerts in the test data and calculate a ranking list for the alerts based on the extracted context features. Top-ranked alerts are identified as critical alerts. Each alert represents an anomaly in the test data.

Patent Agency Ranking