Latest revision as of 12:29, 8 February 2021

Abstract

Current network measurement systems are becoming highly sophisticated, producing huge amounts of convoluted measurement data and statistics. As a very common case, those networks implementing statistics reporting based on the NetFlow [15] technology can generate several GBs of data on a daily basis. In addition, these measurements are often very hard to interpret. In this chapter we describe a method that provides linguistic summaries of network traffic measurements as well as a procedure for finding hidden facts in the form of linguistic association rules. Thus, here we address an association rules mining problem. The method is suitable for summarization and analysis of network measurements at the flow level. As a first step, fuzzy linguistic summaries are applied to analyze and extract concise and human consistent summaries from NetFlow collections. Then, a procedure for mining hidden facts in network flow measurements in the form of fuzzy association rules is developed. The method is applied to a wide set of heterogeneous flow measurements, and is shown to be of practical application to network operation and traffic engineering [6, 5], where it can help solve a number of current issues. © 2011 Springer-Verlag Berlin Heidelberg.

Peer Reviewed

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/10261/83345

• http://link.springer.com/content/pdf/10.1007/978-3-642-18084-2_4.pdf,

http://dx.doi.org/10.1007/978-3-642-18084-2_4

• https://link.springer.com/chapter/10.1007%2F978-3-642-18084-2_4,

http://digital.csic.es/handle/10261/83345,

https://digital.csic.es/handle/10261/83345,

https://rd.springer.com/chapter/10.1007/978-3-642-18084-2_4,

https://academic.microsoft.com/#/detail/69973251

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