Application of machine learned Bayesian networks to detection of anomalies in complex systems
US9349103B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 9, 2013 |
| Grant date | May 24, 2016 |
| Priority date | — |
| Expiry date | Jun 9, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
According to one embodiment, in response to a set of data for anomaly detection, a Bayesian belief network (BBN) model is applied to the data set, including for each of a plurality of features of the BBN model, performing an estimate using known observed values associated with remaining features to generate a posterior probability for the corresponding feature. A scoring operation is performed using a predetermined scoring algorithm on posterior probabilities of all of the features to generate a similarity score, wherein the similarity score represents a degree to which a given event represented by the data set is novel relative to historical events represented by the BBN model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.