Patent · US Active

Methods of unsupervised anomaly detection using a geometric framework

US8544087B1 · kind B1 · utility

58Cited by

47References

58Claims

0Family size

Assignee

The Trustees of Columbia University in the City of New York · US

Inventors

Eleazar Eskin · Santa Monica, US
Andrew Oliver Arnold · New York, US
Michael J. Prerau · Somerville, US
Leonid Portnoy · Brooklyn, US
Salvatore J. Stolfo · Ridgewood, US

Key dates

Filing date	Jan 30, 2008
Grant date	Sep 24, 2013
Priority date	—
Expiry date	Feb 6, 2030

Classification

Technology area (CPC H)Electricity
CPC primaryH04L63/1416
WIPO fieldDigital communication
WIPO sectorElectrical engineering

Abstract

A method for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. Data elements are mapped to a feature space which is typically a vector space . Anomalies are detected by determining which points lies in sparse regions of the feature space. Two feature maps are used for mapping data elements to a feature apace. A first map is a data-dependent normalization feature map which we apply to network connections. A second feature map is a spectrum kernel which we apply to system call traces.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.