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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:
1179789
Patent Number(s):
9,038,180
Application Number:
13/826,995
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Mar 14
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., 2015. 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/1179789.
@article{osti_1179789,
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 = {2015},
month = {5}
}

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