Time series: theory and methods
Time series: theory and methods
On the predictability of large transfer TCP throughput
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Automated Detection of Load Changes in Large-Scale Networks
TMA '09 Proceedings of the First International Workshop on Traffic Monitoring and Analysis
Experiences of VoIP traffic monitoring in a commercial ISP
International Journal of Network Management
M/G/∞ transience, and its applications to overload detection
Performance Evaluation
Multivariate fairly normal traffic model for aggregate load in large-scale data networks
WWIC'10 Proceedings of the 8th international conference on Wired/Wireless Internet Communications
Wide-area Internet traffic patterns and characteristics
IEEE Network: The Magazine of Global Internetworking
Modeling VoIP Call Holding Times for Telecommunications
IEEE Network: The Magazine of Global Internetworking
A methodological overview on anomaly detection
DataTraffic Monitoring and Analysis
Changepoint detection techniques for VoIP traffic
DataTraffic Monitoring and Analysis
Anomaly detection in diurnal data
Computer Networks: The International Journal of Computer and Telecommunications Networking
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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).