Decision Theoretic Approach

Overview

In this project we study and investigate network anomaly detection algorithms [1] [2] [3] for Internet Paths. We also develop a Decision Theoretic Approach based on our observations about the characteristics of the performance measurements statistics obtained from the IEPM-BW project.

To study and compare the algorithms we use the data sets collected by IEPM-BW starting November 2006 up till March 2008 (approximately 12,000 hours). The Internet paths observed were the links between Stanford Linear Accelerator Center (SLAC) and the following sites:

1. San Diego Supercomputing Center (SDSC) USA,
2. Oak Ridge National Laboratory (ORNL) USA,
3. European Organization for Nuclear Research (CERN) Geneva, Switzerland,
4. Forschungszentrum Karlsruhe (FZK) Germany,
5. Deutsches Elektronen- Synchrotron (DESY) Germany and
6. University of Toronto (UTORONTO) Canada.

Data Sets

The data sets used in the study may be downloaded from the links listed below. Latest performance statistics may be accessed from here.

Labeling Algorithm

The labeling algorithm is as under:

Implementations and Parameter Tuning

The source code of the implementations and the tuning of parameters is discussed below.

References

1. C. Logg, L. Cottrell, and J. Navratil. Experiences in traceroute and available bandwidth change analysis. In NetT '04: Proceedings of the ACM SIGCOMM workshop on Network troubleshooting, pages 247-252. ACM, 2004.
2. A. Soule, K. Salamatian, and N. Taft. Combining filtering and statistical methods for anomaly detection. In Internet Measurement Conference (IMC 2005), pages 331-344. USENIX, 2005.
3. H. Hajji. Statistical analysis of network traffic for adaptive faults detection. In IEEE Transactions on Neural Networks, pages 1053-1063, 2005.