METHODS OF UNSUPERVISED ANOMALY DETECTION USING A GEOMETRIC FRAMEWORK
Number of patents in Portfolio can not be more than 2000
United States of America Patent
Stats
-
N/A
Issued Date -
Jun 30, 2016
app pub date -
Mar 8, 2016
filing date -
Mar 8, 2016
priority date (Note) -
Published
status (Latency Note)
Importance
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 d. 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.
First Claim
Family
International Classification(s)
- [Classification Symbol]
- [Patents Count]
Cited Art Landscape
Patent Citation Ranking
Advertisement
Advertisement
Advertisement
Recipient Email Address
Recipient Email Address
Comment
Recipient Email Address
Add to Portfolio(s)
To add this patent to one, or more, of your portfolios, simply click the add button.
This Patent is in these Portfolios:
Add to additional portfolios:
Last Refreshed On:
Changes done successfully