Anomaly detection method and system
AssigneesSamsung Sds Co., Ltd., UIF (University Industry Foundation), Yonsei University
InventorsHak Soo LIM, Min-Jung KIM, Se Won PARK, No Seong PARK
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Abstract
A method for an anomaly detection is provided. The method may include acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density, extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data, and predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.
Claims16
An anomaly detection method performed by at least one computing device, comprising:
- acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density;
- extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data;
- and
- predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.
An anomaly detection method performed by at least one computing device, comprising:
- acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density;
- extracting data for a specific time and first data segments corresponding to a period before the specific time from target time-series data;
- generating second data segments by adjusting the first data segments through the trained score predictor;
- and
- predicting a conditional score for the second data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.
An anomaly detection system comprising:
- at least one processor;
- and
- a memory storing a computer program executed by the at least one processor,
- wherein the computer program includes instructions for performing operations of:
- acquiring a score predictor trained using normal time-series data, wherein the score predictor is a deep learning model configured to output a conditional score for previous time-series data and the conditional score represents a gradient of data density;
- extracting data for a specific time and data segments corresponding to a period before the specific time from target time-series data;
- and
- predicting a conditional score for the data segments through the trained score predictor and conducting an anomaly determination for the data for the specific time using the predicted conditional score.
Dependencies are inferred from the claim text.