Scipediacontent: Scipediacontent moved page Draft Content 262750284 to Fiore Palmieri 2009a

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Scipediacontent moved page Draft Content 262750284 to Fiore Palmieri 2009a

Scipediacontent: Created page with " == Abstract == The ability to accurately classify and identify the network traffic associated with different applications is a central issue for many network operation and r..."

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== Abstract ==

The ability to accurately classify and identify the network traffic associated with different applications is a central issue for many network operation and research topics including Quality of Service enforcement, traffic engineering, security, monitoring and intrusion-detection. However, traditional classification approaches for traffic to higher-level application mapping, such as those based on port or payload analysis, are highly inaccurate for many emerging applications and hence useless in actual networks. This paper presents a recurrence plot-based traffic classification approach based on the analysis of non-stationary "hidden" transition patterns of IP traffic flows. Such nonlinear properties cannot be affected by payload encryption or dynamic port change and hence cannot be easily masqueraded. In performing a quantitative assessment of the above transition patterns, we used recurrence quantification analysis, a nonlinear technique widely used in many fields of science to discover the time correlations and the hidden dynamics of statistical time series. Our model proved to be effective for providing a deterministic interpretation of recurrence patterns derived by complex protocol dynamics in end-to-end traffic flows, and hence for developing qualitative and quantitative observations that can be reliably used in traffic classification. Â© 2008 Elsevier B.V. All rights reserved.

== Original document ==

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

* [http://hdl.handle.net/11367/64001 http://hdl.handle.net/11367/64001]

* [https://api.elsevier.com/content/article/PII:S138912860800409X?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S138912860800409X?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S138912860800409X?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S138912860800409X?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.comnet.2008.12.015 http://dx.doi.org/10.1016/j.comnet.2008.12.015] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://www.sciencedirect.com/science/article/pii/S138912860800409X https://www.sciencedirect.com/science/article/pii/S138912860800409X],
: [https://dblp.uni-trier.de/db/journals/cn/cn53.html#PalmieriF09 https://dblp.uni-trier.de/db/journals/cn/cn53.html#PalmieriF09],
: [http://doi.org/10.1016/j.comnet.2008.12.015 http://doi.org/10.1016/j.comnet.2008.12.015],
: [https://doi.org/10.1016/j.comnet.2008.12.015 https://doi.org/10.1016/j.comnet.2008.12.015],
: [https://dl.acm.org/citation.cfm?id=1518207 https://dl.acm.org/citation.cfm?id=1518207],
: [http://www.sciencedirect.com/science/article/pii/S138912860800409X http://www.sciencedirect.com/science/article/pii/S138912860800409X],
: [https://core.ac.uk/display/53809928 https://core.ac.uk/display/53809928],
: [https://academic.microsoft.com/#/detail/2068754024 https://academic.microsoft.com/#/detail/2068754024]

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Fiore Palmieri 2009a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 262750284 to Fiore Palmieri 2009a

Scipediacontent: Created page with " == Abstract == The ability to accurately classify and identify the network traffic associated with different applications is a central issue for many network operation and r..."