Name
Abstract | ||
|---|---|---|
In this paper we present methodological advances in anomaly detection, which, among other purposes, can be used to discover abnormal traffic patterns under the presence of deterministic trends in data, given that specific assumptions about the traffic type and nature are met. A performance study of the proposed methods, both if these assumptions are fulfilled and violated, shows good results in great generality. Our study features VoIP call counts, but the methodology can be applied to any data following, at least roughly, a non-homogeneous Poisson process (think of highly aggregated traffic flows).
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Year | Venue | Keywords |
|---|---|---|
2012 | International Teletraffic Congress | anomaly detection,traffic type,abnormal traffic pattern,methodological advance,voip call count,aggregated traffic flow,voip traffic,great generality,performance study,good result,deterministic trend,vectors,stochastic processes,computer network security,market research,degradation,exponential distribution,time measurement,data models,internet telephony,correlation |
Field | DocType | ISBN |
Data modeling,Anomaly detection,Computer science,Network security,Computer network,Stochastic process,Exponential distribution,Generality,Market research,Voice over IP | Conference | 978-1-4503-1896-9 |
Citations | PageRank | References |
2 | 0.40 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felipe Mata | 1 | 14 | 2.93 |
| Piotr Zuraniewsk | 2 | 2 | 0.40 |
| Michel Mandjes | 3 | 534 | 73.65 |
| Marco Mellia | 4 | 2748 | 204.65 |