Anomaly detection method, program, and system
AssigneeInternational Business Machines Corporation
InventorsTsuyoshi Ide, Tetsuro Morimura, Bin Tong
Life of this patent
Estimating the term…
Abstract
A method providing an analytical technique introducing label information into an anomaly detection model. The method includes the steps of: inputting measurement data having an anomalous or normal label and measurement data having no label as samples; determining a similarity matrix indicating the relationship between the samples based on the samples; defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty; and calculating a degree of anomaly based on the calculated parameters. The present invention also provides a program and system for detecting an anomaly based on measurement data.
Claims10
A computer implemented method to detect an anomaly based on measurement data, the method comprising the steps of:
- inputting measurement data having an anomalous or normal label and measurement data having no label as samples;
- determining a similarity matrix indicating the relationship between the samples based on the samples, wherein the similarity matrix is a N×N square matrix, where N is the number of samples, each row and each column corresponding to samples, an element corresponding to a normal (labeled) sample and a normal sample being positive number a, an element corresponding to a normal sample and an anomalous sample being non-positive number b, an element corresponding to a normal sample and an unlabeled sample being c, an element corresponding to an anomalous sample and an anomalous sample being d, an element corresponding to an anomalous sample and an unlabeled sample being e, and an element corresponding to an unlabeled sample and an unlabeled sample being f;
- defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty;
- and
- calculating a degree of anomaly based on the calculated parameters.
A computer executed program to detect an anomaly based on measurement data, the program executing in a computer the steps of:
- inputting measurement data having an anomalous or normal label and measurement data having no label as samples;
- determining a similarity matrix indicating the relationship between the samples based on the samples, wherein the similarity matrix is a N×N square matrix, where N is the number of samples, each row and each column corresponding to samples, an element corresponding to a normal (labeled) sample and a normal sample being positive number a, an element corresponding to a normal sample and an anomalous sample being non-positive number b, an element corresponding to a normal sample and an unlabeled sample being c, an element corresponding to an anomalous sample and an anomalous sample being d, an element corresponding to an anomalous sample and an unlabeled sample being e, and an element corresponding to an unlabeled sample and an unlabeled sample being f;
- defining a penalty based on the similarity matrix and calculating parameters in accordance with an updating equation having a term reducing the penalty;
- and
- calculating a degree of anomaly based on the calculated parameters.
A computer implemented system to detect an anomaly based on measurement data, the system comprising:
- storage means;
- measurement data having an anomalous or normal label and measurement data having no label stored in the storage means as samples;
- means for determining a similarity matrix indicating the relationship between the samples based on the samples, wherein the similarity matrix is a N×N square matrix, where N denotes the number of samples, each row and each column corresponding to samples, an element corresponding to a normal (labeled) sample and a normal sample being positive number a, an element corresponding to a normal sample and an anomalous sample being non-positive number b, an element corresponding to a normal sample and an unlabeled sample being c, an element corresponding to an anomalous sample and an anomalous sample being d, an element corresponding to an anomalous sample and an unlabeled sample being e, and an element corresponding to an unlabeled sample and an unlabeled sample being f;
- means for defining a penalty based on the similarity matrix and for calculating parameters in accordance with an updating equation having a term reducing the penalty;
- and
- means for calculating a degree of anomaly based on the calculated parameters.
Dependencies are inferred from the claim text.