Prioritized detection and classification of clusters of anomalous samples on high-dimensional continuous and mixed discrete/continuous feature spaces
US10846308B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 26, 2017 |
| Grant date | Nov 24, 2020 |
| Priority date | — |
| Expiry date | Jul 26, 2037 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2463/144
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This patent concerns novel technology for detection of zero-day data classes for domains with high-dimensional mixed continuous/discrete feature spaces, including Internet traffic. Assume there is a known-class database available for learning a null hypothesis that a given new batch of unlabeled data does not contain any data from unknown/anomalous classes. A novel and effective generalization of previous parsimonious mixture and topic modeling methods is developed. The novel unsupervised anomaly detector (AD) acts on a new unlabeled batch of data to either identify the statistically significant anomalous classes latently present therein or reject the alternative hypothesis that the new batch contains any anomalous classes. The present AD invention can be applied in an on-line setting. Labeling (by a human expert or by other means) of anomalous clusters provides new supervised data that can be used to adapt an actively learned classifier whose objective is to discriminate all the classes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.