DocumentCode :
1168565
Title :
Load characterization and anomaly detection for voice over IP traffic
Author :
Mandjes, Michel ; Saniee, Iraj ; Stolyar, Alexander L.
Author_Institution :
Center for Math. & Comput. Sci., Amsterdam, Netherlands
Volume :
16
Issue :
5
fYear :
2005
Firstpage :
1019
Lastpage :
1026
Abstract :
We consider the problem of traffic anomaly detection in IP networks. Traffic anomalies typically arise when there is focused overload or when a network element fails and it is desired to infer these purely from the measured traffic. We derive new general formulae for the variance of the cumulative traffic over a fixed time interval and show how the derived analytical expression simplifies for the case of voice over IP traffic, the focus of this paper. To detect load anomalies, we show it is sufficient to consider cumulative traffic over relatively long intervals such as 5 min. We also propose simple anomaly detection tests including detection of over/underload. This approach substantially extends the current practice in IP network management where only the first-order statistics and fixed thresholds are used to identify abnormal behavior. We conclude with the application of the scheme to field data from an operational network.
Keywords :
Internet telephony; statistical analysis; telecommunication network management; telecommunication traffic; 5 min; IP network management; anomaly detection tests; first-order statistics; fixed thresholds; heavy-tailed holding times; load characterization; second-order statistic; traffic anomaly detection; traffic measurement; voice-over IP traffic; Analysis of variance; IP networks; Internet telephony; Load management; Speech analysis; Statistics; Switches; Telecommunication traffic; Testing; Traffic control; Anomaly detection; heavy-tailed holding times; load characterization; network management; second-order statistic; traffic measurements; voice-over IP; Algorithms; Artifacts; Artificial Intelligence; Computer Simulation; Information Storage and Retrieval; Internet; Models, Statistical; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Telecommunications;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2005.853427
Filename :
1510706
Link To Document :
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