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Fig. 2: ROC curves for Plateau (PL), |
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a) Plateau Algorithm
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The [Plateau Algorithm|http://www.acm.org/sigs/sigcomm/sigcomm2004/workshop_papers/nts26-logg1.pdf] \[1\] takes four input parameters - history buffer length, trigger buffer length, sensitivity and threshold. We define the trigger buffer length to hold observations spanning at least 3 hours (since we define an anomalous event as one that persists for at least 3 hours). Since the measurements made by IEPM are 45 mins apart, we get about 6 observations. We then empirically determine the optimal length of the history buffer to span at least 5 days ~ 240 observations. |
We initially define the sensitivity as one (1) and determine the optimal threshold value for each link (i.e. the value with the optimal ratio of true-positives to false positives). As suggested by the authors, this threshold falls between 30%-40%. Finally for the history buffer length, the trigger buffer length and the optimal threshold, we vary the sensitivity to determine 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 2.
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b) Adaptive Fault Detection
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The Adaptive Fault Detection \[3\] 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.
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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
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The Kalman Filter \[2\] 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.
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