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

Abstract

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.

Inventors:
; ; ; ;
Issue Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1247988
Patent Number(s):
9319421
Application Number:
14/053,248
Assignee:
UT-Battelle, LLC (Oak Ridge, TN)
Patent Classifications (CPCs):
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04L - TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Oct 14
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS

Citation Formats

Ferragut, Erik M., Goodall, John R., Iannacone, Michael D., Laska, Jason A., and Harrison, Lane T. Real-time detection and classification of anomalous events in streaming data. United States: N. p., 2016. Web.
Ferragut, Erik M., Goodall, John R., Iannacone, Michael D., Laska, Jason A., & Harrison, Lane T. Real-time detection and classification of anomalous events in streaming data. United States.
Ferragut, Erik M., Goodall, John R., Iannacone, Michael D., Laska, Jason A., and Harrison, Lane T. Tue . "Real-time detection and classification of anomalous events in streaming data". United States. https://www.osti.gov/servlets/purl/1247988.
@article{osti_1247988,
title = {Real-time detection and classification of anomalous events in streaming data},
author = {Ferragut, Erik M. and Goodall, John R. and Iannacone, Michael D. and Laska, Jason A. and Harrison, Lane T.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {4}
}

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Works referenced in this record:

Integration of Self-Organizing Map (SOM) and Kernel Density Estimation (KDE) for network intrusion detection
conference, September 2009


Anomaly detection: A survey
journal, July 2009


VAST Challenge 2012: Visual analytics for big data
conference, October 2012


An Intrusion-Detection Model
journal, February 1987