Anomaly detection method, program, and system

Patent metadata

Field	Value
Publication number	US-9824069-B2
Application number	US-201615153090-A
Country	US
Kind code	B2
Filing date	May 12, 2016
Priority date	Jun 14, 2012
Publication date	Nov 21, 2017
Grant date	Nov 21, 2017

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.

First claim

What is claimed is:

1. 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, wherein measurement data serving as samples use a model determined by a latent variable, an inner product of parameters and a noise term;
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.

2. The method according to claim 1 , wherein the noise term depends on whether a sample is a normal sample, anomalous sample, or unlabeled sample, so that the noise term of a normal sample ≦the noise term of an unlabeled sample ≦the noise term of an anomalous sample.

3. 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, wherein measurement data serving as samples use a model determined by a latent variable, an inner product of parameters and a noise term;
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.

4. The program according to claim 3 , wherein the noise term depends on whether a sample is a normal sample, anomalous sample, or unlabeled sample, so that the noise term of a normal sample ≦the noise term of an unlabeled sample ≦the noise term of an anomalous sample.

5. 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, wherein measurement data serving as samples use a model determined by a latent variable, an inner product of parameters and a noise term;
means for determining a similarity matrix indicating the relationship between the samples based on the samples;
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.

Assignees

• IBM

Inventors

• Ide Tsuyoshi
• Morimura Tetsuro
• Tong Bin

Classifications

• G06F2218/12

  Classification; Matching · CPC title

• G06F18/2433 Primary

  Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection · CPC title

• G06F17/18 Primary

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06K9/6267

  Physics · mapped topic