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| Raw data | Labeled data | |||||
|---|---|---|---|---|---|---|---|
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="b91e749cd1476bc0-904ea665-467f4276-bca4b825-bce4b189f1c6ccb469320e25"><ac:plain-text-body><![CDATA[ | 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]] | ]]></ac:plain-text-body></ac:structured-macro> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="835287da4bce9fd1-dc098351-49014906-8df8bb98-50755e8d8b8598af99daf3c2"><ac:plain-text-body><![CDATA[ | 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]] | ]]></ac:plain-text-body></ac:structured-macro> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="033b1680d02ede18-1bf8d847-44e84f63-9edd9450-52f816d1e4176f6b23a6551b"><ac:plain-text-body><![CDATA[ | 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> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="e258d7150d797e91-c06626e8-4af44191-bff0b6d3-dc0f9f0b01a75728e8e1c639"><ac:plain-text-body><![CDATA[ | 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]] | ]]></ac:plain-text-body></ac:structured-macro> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="a5570493b82b04fd-2447e5b7-4c34421b-82dd97f3-f2177f4cb1ed06fa50aa7f4b"><ac:plain-text-body><![CDATA[ | 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> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="d88d02f8bd3e814d-5688f29f-430d4c36-84019ee3-1121f10b53f0dcc255f023a0"><ac:plain-text-body><![CDATA[ | TRIUMF | [[csv | http://www.slac.stanford.edu/~kalim/event-detection/published-data/TRIUMF-pathchirp.csv]], [[xls | http://www.slac.stanford.edu/~kalim/event-detection/published-data/TRIUMF-pathchirp.xls]] | [[txt | http://www.slac.stanford.edu/~kalim/event-detection/published-data/TRIUMF-pathchirp-labeled-events.txt]] | ]]></ac:plain-text-body></ac:structured-macro> |
<ac:structured-macro ac:name="unmigrated-wiki-markup" ac:schema-version="1" ac:macro-id="25853a7dbfe8c8e1-03866150-4e86450a-94689d7e-74f48fa9edca4150af2d1a27"><ac:plain-text-body><![CDATA[ | 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] | ]]></ac:plain-text-body></ac:structured-macro> |
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b) Adaptive Fault Detection
Adaptive Fault Detection
c) Kalman Filters
The Adaptive Fault Detection method employs four parameters - the desidred detection rate, the desired false positive rate, the history buffer length and the observed window length. We use the values as suggested by the author and for observed window length of 20, a history buffer length of 100 and the desired detection rate of 0.95 and we vary the desired false positive rate to obtain the spectrum of the ratio of true-positive rate to false positive rate. Based on the observations we plot the ROC curves as shown in figure 2. One set of data points is shown in table 3.
Table 3: Results compiled by Adaptive Fault Detection (ADF) method when analyzing the Internet path between SLAC and DESY.
N | HN | B | a | TP | FP | #P | N~days | TPR | FPR |
|---|---|---|---|---|---|---|---|---|---|
20 | 100 | 0.95 | 0.0300 | 0 | 31 | 31 | 672 | 0 | 0.046 |
20 | 100 | 0.95 | 0.0200 | 2 | 90 | 31 | 672 | 0.065 | 0.134 |
20 | 100 | 0.95 | 0.0090 | 6 | 619 | 31 | 672 | 0.194 | 0.921 |
20 | 100 | 0.95 | 0.0080 | 9 | 796 | 31 | 672 | 0.29 | 1.185 |
20 | 100 | 0.95 | 0.0070 | 16 | 994 | 31 | 672 | 0.516 | 1.479 |
20 | 100 | 0.95 | 0.0050 | 23 | 1672 | 31 | 672 | 0.742 | 2.488 |
20 | 100 | 0.95 | 0.0040 | 23 | 2179 | 31 | 672 | 0.742 | 3.243 |
20 | 100 | 0.95 | 0.0030 | 29 | 2837 | 31 | 672 | 0.935 | 4.222 |
20 | 100 | 0.95 | 0.0023 | 30 | 3528 | 31 | 672 | 0.968 | 5.25 |
20 | 100 | 0.95 | 0.0022 | 31 | 3611 | 31 | 672 | 1 | 5.374 |
Legend:
HN - History buffer length, a - desired false positive rate, B - desired detection rate, N - observed window length, TP - true positives obtained, FP - false positives obtained, #P - true positives, N~days - days observed, TPR - true positive rate, FPR - false positive rate.
| Wiki Markup |
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The source code of the implementation is available in [C#|http://www.slac.stanford.edu/~kalim/event-detection/published-src/c-afd-impl.html] \[[cs|http://www.slac.stanford.edu/~kalim/event-detection/published-src/afd.cs]\]. |
c) Kalman Filters
The Kalman Filter method employs two input parameters - the observed window length and the Kalman gain. We use the values as suggested by the author and for the observed window length and vary the kalman gain to obtain the spectrum of the ratio of true-positive rate to false positive rate. Based on the observations we plot the ROC curves as shown in figure 2. One set of data points is shown in table 4.
Table 4: Results compiled by Kalman Filters (KF) method when analyzing the Internet path between SLAC and DESY.
W | K | TP | FP | #P | N~days | TPR | FPR |
|---|---|---|---|---|---|---|---|
3 | 0.800 | 1 | 309 | 31 | 672 | 0.032 | 0.46 |
6 | 0.500 | 2 | 333 | 31 | 672 | 0.065 | 0.496 |
6 | 0.100 | 3 | 366 | 31 | 672 | 0.097 | 0.545 |
6 | 0.005 | 3 | 371 | 31 | 672 | 0.097 | 0.552 |
6 | 0.001 | 3 | 371 | 31 | 672 | 0.097 | 0.552 |
Legend:
W- observed window length, K - kalman gain, TP - true positives obtained, FP - false positives obtained, #P - true positives, N~days - days observed, TPR - true positive rate, FPR - false positive rate.
| Wiki Markup |
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The source code of the implementation is available in [C#|http://www.slac.stanford.edu/~kalim/event-detection/published-src/c-kf-impl.html] \[[cs|http://www.slac.stanford.edu/~kalim/event-detection/published-src/kf.cs]\]. (not up to date, will be revised soon) |
d) Decision Theoretic Approach
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