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In this project we study and investigate network anomaly detection algorithms \[1\] \[2\] \[3\] for Internet Paths. We also develop a _Decision Theoretic Approach_ (DTA) based on our observations about the characteristics of the performance measurements statistics obtained from the [IEPM-BW] project.

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  1. San Diego Supercomputing Center (SDSC) USA,
  2. Oak Ridge National Laboratory (ORNL) USA,
  3. European Organization for Nuclear Research (CERN) Geneva, Switzerland,
  4. Forschungszentrum Karlsruhe (FZK) Germany,
  5. Deutsches Elektronen- Synchrotron (DESY) Germany and
  6. University of Toronto (UTORONTO) Canada.

The topology of the monitoring framework is shown in figure 1.

Fig. 1: Topology of IEPM as of 07/2008

Image Added

Data Sets

The data sets used in the study may be downloaded from the links listed below. These data sets were collected by the IEPM-BW project and the latest performance statistics may be accessed from here.

 

Raw data

Labeled data

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SDSC

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/SDSC-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/SDSC-pathchirp.xls]]

[[txt

http://www.slac.stanford.edu/~kalim/event-detection/published-data/UTORONTO-pathchirp-labeled-events.txt]]

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CERN

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/CERN-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/CERN-pathchirp.xls]]

[[txt

http://www.slac.stanford.edu/~kalim/event-detection/published-data/CERN-pathchirp-labeled-events.txt]]

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FZK

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/FZK-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/FZK-pathchirp.xls]]

[[txt

http://www.slac.stanford.edu/~kalim/event-detection/published-data/FZK-pathchirp-labeled-events.txt]]

]]></ac:plain-text-body></ac:structured-macro>

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DESY

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/DESY-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/DESY-pathchirp.xls]]

[[txt

http://www.slac.stanford.edu/~kalim/event-detection/published-data/DESY-pathchirp-labeled-events.txt]]

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UTORONTO

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/UTORONTO-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/UTORONTO-pathchirp.xls]]

[[txt

http://www.slac.stanford.edu/~kalim/event-detection/published-data/UTORONTO-pathchirp-labeled-events.txt]]

]]></ac:plain-text-body></ac:structured-macro>

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ORNL

[[csv

http://www.slac.stanford.edu/~kalim/event-detection/published-data/ORNL-pathchirp.csv]], [[xls

http://www.slac.stanford.edu/~kalim/event-detection/published-data/ORNL-pathchirp.xls]]

[txt]

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Wiki Markup
Download the complete data archive \[[zip|http://www.slac.stanford.edu/~kalim/event-detection/published-data/published-data.zip] 11 MB\] or \[[7z|http://www.slac.stanford.edu/~kalim/event-detection/published-data/published-data.7z] 7.2 MB\]

Labeling

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and Detection Algorithms

Wiki Markup
To perform a fair comparison between \[1\], \[2\], \[3\] and the proposed DTA we devised a labeling algorithm to identify true anomalies in the data sets. This labeled data was then used to determine the accuracy (true-positive rate), false-positive rate and the detection delay. The labeling algorithm and the decision theoretic approach for real-time anomaly detection are discussed in the research paper. (The paper will be posted soon.)
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The labeling algorithm is as under:

Implementations and Parameter Tuning

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