Overview
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To study and compare the algorithms we use the data sets collected by IEPM-BW spanning approximately 3 years (i.e. 2005 - 2008). The Internet paths observed were the links between Stanford Linear Accelerator Center (SLAC) and the following sites:
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Fig. 1: Topology of IEPM as of 07/2008 | Fig. 2: Deployment of Selected Sites |
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The number of measurements made to the following sites from SLAC:
Site | pathchirp | iperf | thrulay |
|---|---|---|---|
cern.ch | 48647 | 24586 | 39510 |
desy.de | 32247 | 4522 | 28689 |
fzk.de | 65536 | 4874 | 42708 |
nslabs.ufl.edu | 41206 | 1549 | 28613 |
switch.edu | 19668 | 4638 | 28744 |
sdsc.edu | 21176 | 4416 | 22456 |
triumf.ca | 26425 | 4669 | 27021 |
utoronto.ca | 40614 | 5003 | 21646 |
ornl.gov | 35339 | 5182 | 18375 |
anl.gov | 17968 | 1 | 27559 |
bnl.org | 23580 | 20708 | 16000 |
cacr.caltech.edu | 61871 | 25525 | 37293 |
dl.ac.uk | 27806 | 6096 | 28058 |
nsk.su | 20117 | 1 | 26845 |
cesnet.cz | 23618 | 3062 | 28426 |
infn.it | 30372 | 4343 | 28573 |
ultralight.caltech | 3739 | 88 | 1534 |
SubTotal | 539929 | 119263 | 452050 |
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
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| Data Sets with Events | Data Sets with no Events | <ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="15830d92-3a93-47c9-aec1-e9d646aef18b"><ac:plain-text-body><![CDATA[|
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IEPM | [ [rar^datasets-with-events.rar] ] 3.4 MB, [ [zip^datasets-with-events.zip] ] 3.6 MB | ]]></ac:plain-text-body></ac:structured-macro> |
All files with name "filename_raw_dataset.pathchirp" contain the raw data i.e the available bandwidth measurements along with the timestamps which are used in all algorithms.
All files with name "filename_event_file.txt" contain the list of events identified.
Technical Report - Labeling and Comparative Analysis
The technical report titled "A performance evaluation of anomaly detection algorithms for Internet Paths" will be available soon.
Input/Tuning parameters
Plateau Algorithm (PL)
History Buffer Length (H) | Trigger Buffer Length (T) | Threshold (th) | Sensitivity (s) |
|---|---|---|---|
240 | 6 - 45 | 0.10 - 0.70 | 1.0 - 2.8 |
Kalman Filters Method (KF)
Sensitivity (K) | Time Window (h) |
|---|---|
0.001 - 11.0 | 6 - 20 |
Holt Winter's Method (HW)
? - alpha | ? - beta | ? - gamma | ? - sigma |
|---|---|---|---|
0.1 | 0.1 - 0.3 | 0.1 - 0.5 | 2.0 |
Adaptive Fault Detector (AFD)
Window Size (N) | ? - alpha | ? - beta | No. of Training Data (Hn) |
|---|---|---|---|
20 | 0.95 | 0.0015 - 0.1 | 100 |
Decision Theoretic Approach (DTA)
History Buffer Length (N) | ? - alpha | ? - beta | Median filter length ( n) |
|---|---|---|---|
30 - 90 | 0.01 - 0.34 | 0.99 | 100 |
ROC Results
Datasets with Gaussian Distributions
CERN | FZK | SDSC |
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TRIMUF | UTORONTO |
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Datasets with Weibull Distributions
DESY | NSLABS | SWITCH |
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