In this paper we focus on passive measurements of TCP traffic, main component of nowadays traffic. We propose a heuristic technique for the classification of the anomalies that may occur during the lifetime of a TCP flow, such as out-of-sequence and duplicate segments. Since TCP is a closed-loop protocol that infers network conditions by means of losses and reacts accordingly, the possibility of carefully distinguishing the causes of anomalies in TCP traffic is very appealing, since it may be instrumental to the deep understanding of TCP behavior in real environments and to protocol engineering as well. We apply the proposed heuristic to traffic traces collected at both networks edges and backbone links. By studying the statistical properties of TCP anomalies, we find that their aggregate exhibits Long Range Dependence phenomena, but that anomalies suffered by individual long-lived flows are on the contrary uncorrelated. Interestingly, no dependence to the actual link load is observed.

Passive Identification and Analysis of TCP Anomalies / Mellia, Marco; Meo, Michela; Muscariello, L.; Rossi, D.. - STAMPA. - 2:(2006), pp. 723-728. ((Intervento presentato al convegno Communications, 2006. ICC '06. IEEE International Conference on nel Giugno 2006 [10.1109/ICC.2006.254793].

Passive Identification and Analysis of TCP Anomalies

MELLIA, Marco;MEO, Michela;
2006

Abstract

In this paper we focus on passive measurements of TCP traffic, main component of nowadays traffic. We propose a heuristic technique for the classification of the anomalies that may occur during the lifetime of a TCP flow, such as out-of-sequence and duplicate segments. Since TCP is a closed-loop protocol that infers network conditions by means of losses and reacts accordingly, the possibility of carefully distinguishing the causes of anomalies in TCP traffic is very appealing, since it may be instrumental to the deep understanding of TCP behavior in real environments and to protocol engineering as well. We apply the proposed heuristic to traffic traces collected at both networks edges and backbone links. By studying the statistical properties of TCP anomalies, we find that their aggregate exhibits Long Range Dependence phenomena, but that anomalies suffered by individual long-lived flows are on the contrary uncorrelated. Interestingly, no dependence to the actual link load is observed.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/1414007
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