Invention Grant
- Patent Title: Discovering critical alerts through learning over heterogeneous temporal graphs
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Application No.: US15810960Application Date: 2017-11-13
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Publication No.: US10409669B2Publication Date: 2019-09-10
- Inventor: Bo Zong , LuAn Tang , Qi Song , Biplob Debnath , Hui Zhang , Guofei Jiang
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: JP
- Assignee: NEC CORPORATION
- Current Assignee: NEC CORPORATION
- Current Assignee Address: JP
- Agent Joseph Kolodka
- Main IPC: G06F11/07
- IPC: G06F11/07 ; G06F11/34 ; G06N3/08 ; G06N5/04 ; G06N5/02

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.
Public/Granted literature
- US20180137001A1 DISCOVERING CRITICAL ALERTS THROUGH LEARNING OVER HETEROGENEOUS TEMPORAL GRAPHS Public/Granted day:2018-05-17
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