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Publications by Type: Journal and Magazine Articles

Spatio-Temporal Network Anomaly Detection by Assessing Deviations of Empirical Measures
Zitatschlüssel PS-STNAD-09
Autor Paschalidis, Ioannis Ch. and Smaragdakis, Georgios
Seiten 685–697
Jahr 2009
ISSN 1063-6692
DOI http://dx.doi.org/10.1109/TNET.2008.2001468
Journal IEEE/ACM Transactions on Networking
Jahrgang 17
Nummer 3
Monat June
Zusammenfassung We introduce an Internet traffic anomaly detection mechanism based on large deviations results for empirical measures. Using past traffic traces we characterize network traffic during various time-of-day intervals, assuming that it is anomaly-free. We present two different approaches to characterize traffic: (i) a model-free approach based on the method of types and Sanov's theorem, and (ii) a model-based approach modeling traffic using a Markov modulated process. Using these characterizations as a reference we continuously monitor traffic and employ large deviations and decision theory results to ''compare'' the empirical measure of the monitored traffic with the corresponding reference characterization, thus, identifying traffic anomalies in real-time. Our experimental results show that applying our methodology (even short-lived) anomalies are identified within a small number of observations. Throughout, we compare the two approaches presenting their advantages and disadvantages to identify and classify temporal network anomalies. We also demonstrate how our framework can be used to monitor traffic from multiple network elements in order to identify both spatial and temporal anomalies. We validate our techniques by analyzing real traffic traces with time-stamped anomalies.
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