In this paper we propose to face the problem of classifying IP flows by means of different pattern recognition approaches. They have been explicitly devised in order to effectively address the problem of the unknown classes, too. All experimental evaluation of the various proposal oil real traffic traces is also provided. by considering different network scenarios.

The assignment of an IP flow to a class, according to the application that generated it, is at the basis of any modern network management platform. However, classification techniques such as the ones based oil the analysis of transport layer or application layer information are rapidly becoming ineffective. Moreover, in several network scenarios it is quite unrealistic to assume that all the classes an IP flow call belong to are a priori known. In these cases. in fact, some network protocols may be known, but novel protocols call appear so giving rise to unknown classes.

Pattern Recognition Approaches for Classifying IP Flows

Gargiulo Francesco;
2008

Abstract

The assignment of an IP flow to a class, according to the application that generated it, is at the basis of any modern network management platform. However, classification techniques such as the ones based oil the analysis of transport layer or application layer information are rapidly becoming ineffective. Moreover, in several network scenarios it is quite unrealistic to assume that all the classes an IP flow call belong to are a priori known. In these cases. in fact, some network protocols may be known, but novel protocols call appear so giving rise to unknown classes.
2008
978-3-540-89688-3
In this paper we propose to face the problem of classifying IP flows by means of different pattern recognition approaches. They have been explicitly devised in order to effectively address the problem of the unknown classes, too. All experimental evaluation of the various proposal oil real traffic traces is also provided. by considering different network scenarios.
Pattern Recognition
IP flows
classification
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/317159
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