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Title: Real-time detection and classification of anomalous events in streaming data

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. The system can include a plurality of anomaly detectors that together implement an algorithm to identify low probability events and detect atypical traffic patterns. The atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.
Authors:
; ; ; ;
Publication Date:
OSTI Identifier:
1247988
Report Number(s):
9,319,421
14/053,248
DOE Contract Number:
AC05-00OR22725
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Oct 14
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS