Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Generalized Pareto Mixture Distribution Approach to Network Modeling and Performance Evaluation 1



Z Q. Lu, N Sedransk


A recurring theme in Internet network traffic analysis is that most observed data show characteristics of a long (heavy) right-tail distribution taking on nonnegative values. Thus, extreme value distribution such as generalized Pareto distribution (GPD) provides a natural setup for modeling the tail behavior of network data. On the other hand, due to the enormous diversity in network traffic at different time scales, at different nodes, different sources or protocols, it is necessary to introduce a more flexible framework using finite mixtures. We believe that the generalized Pareto mixture distribution (GPMD) is such a general model and it should form a theoretical basis for any anomaly detection algorithms for extreme events detection. We will demonstrate these points through applying to the popular RTT pingER data. For example, in terms of pingER RTT performance, users might have felt more frequent network slowdowns even if the median performance improved after a NIST network upgrade. Potential extensions to include temporal dependence and continuous time process are also discussed. Our GPMD methodology as a powerful tool for detecting perturbations at extreme events may prove useful in other areas of network security.
IEEE Transactions on Signal Processing


Anomaly detection in extreme events, extreme value theory, finite mixtures, generalized Pareto distribution, network latency, quantile estimation, tail metrics


Lu, Z. and Sedransk, N. (2021), Generalized Pareto Mixture Distribution Approach to Network Modeling and Performance Evaluation 1, IEEE Transactions on Signal Processing (Accessed April 24, 2024)
Created October 12, 2021