Accurate, Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets

Valenti, Silvio; Rossi, Dario; Meo, Michela; Mellia, Marco; Bermolen, Paola

doi:10.1007/978-3-642-01645-5_10

Cited by 28 publications

(26 citation statements)

References 10 publications

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“…However, the issue of identifying the traffic generated by these applications is an unexplored field, with the only exception of [7] in which authors explicitely target P2PTV applications.…”

Section: Related Workmentioning

confidence: 99%

Comparing P2PTV Traffic Classifiers

Cascarano

Risso

Este

et al. 2010

2010 IEEE International Conference on Communications

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Peer-to-Peer IP Television (P2PTV) applications represent one of the fastest growing application classes on the Internet, both in terms of their popularity and in terms of the amount of traffic they generate. While network operators require monitoring tools that can effectively analyze the traffic produced by these systems, few techniques have been tested on these mostly closed-source, proprietary applications.In this paper we examine the properties of three traffic classifiers applied to the problem of identifying P2PTV traffic. We report on extensive experiments conducted on traffic traces with reliable ground truth information, highlighting the benefits and shortcomings of each approach. The results show that not only their performance in terms of accuracy can vary significantly, but also that their usability features suggest different effective aspects that can be integrated.

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Section: Related Workmentioning

confidence: 99%

Comparing P2PTV Traffic Classifiers

Cascarano

Risso

Este

et al. 2010

2010 IEEE International Conference on Communications

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“…They carried out classification on real-traffic traces of HTTP, SMTP, eDonkey, P2P-TV, MSN messenger, PPlive & two multi-player games; whose traces were verified manually as well as using DPI technique, to achieve recall rates ranging from 90.23 to 100 %. Valenti et al [81] adopted a mechanism based on Support Vector Machine (SVM) and number of packets exchanged between peers during short interval of time; to identify P2P-TV applications. They tested their approach on traffic captured in larger test-bed to achieve recall rates ranging from 91.3 to 99.6 %.…”

Section: Classification Of Traffic In the Darkmentioning

confidence: 99%

Identifying P2P traffic: A survey

Bhatia

Kumar

2016

Peer-to-Peer Netw. Appl.

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Peer-to-Peer (P2P) traffic is widely used for the purpose of streaming media, file-sharing, instant messaging, games, software etc., which often involves copyrighted data. From the past decade, P2P traffic has been contributing to major portion of Internet traffic which is still rising and hence is consuming a lot of network traffic bandwidth. It also worsens congestion of network traffic significantly and degrades the performance of traditional client-server applications. Popularity of various P2P applications has led Internet Service Providers (ISPs) to face various challenges regarding efficiently and fairly utilizing network resources. The traditional methods of identifying P2P traffic such as port-based and payload-based are proving ineffective due to their significant limitations and can be bypassed. Hence, new approaches based on statistics or behaviour of network traffic needs to be developed and adopted in order to accurately identify existing and new P2P traffic which emerge over the time. This article presents a survey regarding various strategies involved in identifying P2P traffic. Furthermore, conceptual analysis of network traffic measurement and monitoring is also presented.

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“…Several studies and experiments have been done to analyse P2PTV applications [1], [2], [3], [4], [5], [6], [7]. Rossi et al…”

Section: Related Workmentioning

confidence: 99%