Accurate, scalable in-network identification of p2p traffic using application signatures

By Subhabrata Sen, Oliver Spatscheck, Dongmei Wang. The Web Conference, 2004

918 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/fcf32c0bfb0358aa88580c2f5a94b76490d984d3

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

A survey of techniques for internet traffic classification using machine learning (2008, background): “[7] showed that payload based classification of P2P traffic (by examining the signatures of the traffic at the application level) could reduce false positives and false negatives to 5% of total bytes for most P2P protocols studied.”
Automated traffic classification and application identification using machine learning (2005, methodology): “The authors of [4] propose signature-based methods to classify P2P traffic.”
Internet traffic classification demystified: myths, caveats, and the best practices (2008, methodology): “To establish a reference point in evaluating the algorithms, we used the payload-based classifier developed in [25], which we augmented with more payload signatures from [37, 16, 41] and manual payload inspection.”
PeerRush: Mining for unwanted P2P traffic (2013, background): “While there exists a vast body of work dedicated to P2P traffic detection [4], a large portion of previous work focuses on signature-based approaches that require deep packet inspection (DPI), or on port-number-based identification [17, 7].”
P4p: provider portal for applications (2008, background): “These devices rely on deep packet inspection or other P2P traffic identification schemes (e.g., [11, 31 ]).”
Pollution in P2P file sharing systems (2005, methodology): “[20] talks about P2P application specific signatures; these signature techniques could be deployed by an ISP to identify and filter illicit P2P traffic.”
BLINC: multilevel traffic classification in the dark (2005, methodology): “Payload-based classiﬁcation approaches tailored to p2p traﬃc have been presented in [19, 9], while identiﬁcation of p2p traﬃc through transport layer characteristics is proposed in [8].”
Traffic classification using clustering algorithms (2006, methodology): “Many recent studies confirm that port-based identification of network traffic is ineffective [8, 15].”
ACAS: automated construction of application signatures (2005, background): “, [11, 4, 7]) involved a labor-intensive process combining information from available documentation with information gleaned from analysis of packet-level traces to develop potential signatures, and using multiple iterations to improve the accuracy and computation overhead.”
A Survey on Internet Traffic Identification (2009, background): “In [46], Sen et al provide an interesting investigation on payload-based P2P traf ﬁ c classi ﬁ cation.”
Offline/realtime traffic classification using semi-supervised learning (2007, background): “Studies show that these approaches work very well for today’s Internet traffic, including P2P flows [15,28].”