skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Using new edges for anomaly detection in computer networks

Abstract

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

Inventors:
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1459412
Patent Number(s):
10,015,183
Application Number:
15/637,475
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 2017 Jun 29
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Neil, Joshua Charles. Using new edges for anomaly detection in computer networks. United States: N. p., 2018. Web.
Neil, Joshua Charles. Using new edges for anomaly detection in computer networks. United States.
Neil, Joshua Charles. Tue . "Using new edges for anomaly detection in computer networks". United States. https://www.osti.gov/servlets/purl/1459412.
@article{osti_1459412,
title = {Using new edges for anomaly detection in computer networks},
author = {Neil, Joshua Charles},
abstractNote = {Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

Patent:

Save / Share:

Works referenced in this record:

Systems And Methods For A Simulated Network Attack Generator
patent-application, December 2009


Adaptive ROC-based ensembles of HMMs applied to anomaly detection
journal, January 2012


A survey of coordinated attacks and collaborative intrusion detection
journal, February 2010

  • Zhou, Chenfeng Vincent; Leckie, Christopher; Karunasekera, Shanika
  • Computers & Security, Vol. 29, Issue 1, p. 124-140
  • DOI: 10.1016/j.cose.2009.06.008

Alert correlation in a cooperative intrusion detection framework
conference, January 2002


Bayesian anomaly detection methods for social networks
journal, August 2010

  • Heard, Nicholas A.; Weston, David J.; Platanioti, Kiriaki
  • The Annals of Applied Statistics, Vol. 4, Issue 2, p. 645-662
  • DOI: 10.1214/10-AOAS329

Botnets: A survey
journal, February 2013

  • Silva, Sérgio S. C.; Silva, Rodrigo M. P.; Pinto, Raquel C. G.
  • Computer Networks, Vol. 57, Issue 2, p. 378-403
  • DOI: 10.1016/j.comnet.2012.07.021

Identifying botnets by capturing group activities in DNS traffic
journal, January 2012


The link-prediction problem for social networks
journal, January 2007

  • Liben-Nowell, David; Kleinberg, Jon
  • Journal of the American Society for Information Science and Technology, Vol. 58, Issue 7, p. 1019-1031
  • DOI: 10.1002/asi.20591

Discovering Collaborative Cyber Attack Patterns Using Social Network Analysis
conference, January 2011

  • Du, Haitao; Yang, Shanchieh Jay; Salerno, John
  • Social Computing, Behavioral-Cultural Modeling and Prediction, p. 129-136
  • DOI: 10.1007/978-3-642-19656-0_20

Exploiting dynamicity in graph-based traffic analysis: techniques and applications
conference, January 2009

  • Iliofotou, Marios; Faloutsos, Michalis; Mitzenmacher, Michael
  • CoNEXT '09 Proceedings of the 5th international conference on Emerging networking experiments and technologies, p. 241-252
  • DOI: 10.1145/1658939.1658967

Scan Statistics for the Online Detection of Locally Anomalous Subgraphs
journal, August 2013


Detecting Anomalies Using End-to-End Path Measurements
conference, April 2008

  • Naidu, K. V. M.; Panigrahi, D.; Rastogi, R.
  • IEEE INFOCOM 2008 - The 27th Conference on Computer Communications
  • DOI: 10.1109/INFOCOM.2008.248

Anomaly detection: A survey
journal, July 2009

  • Chandola, Varun; Banerjee, Arindam; Kumar, Vipin
  • ACM Computing Surveys, Vol. 41, Issue 3, p. 1-58
  • DOI: 10.1145/1541880.1541882