Revealing skype traffic: when randomness plays with you

By Dario Bonfiglio, Marco Mellia, Michela Meo, Dario J. Rossi, Paolo Tofanelli. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2007

356 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey on Internet Traffic Identification (2009, background): “For some papers regarding Skype detection ongoing attempts, see [57][58].”
On dominant characteristics of residential broadband internet traffic (2009, neutral): “Copyright 2009 ACM 978-1-60558-770-7/09/11 ...$10.00.”
Network Traffic Classification Using Correlation Information (2013, methodology): “[18] proposed to analyze the message content randomness introduced by the encryption processing using Pearson’s chi-Square test-based technique.”
A survey of techniques for internet traffic classification using machine learning (2008, methodology): “[67] Naive Bayes and Pearson’s Chi-Squate test • Message size (the length of the message encapsulated into the transport layer protocol segment) • Average inter packet gap Two self collected datasets Skype traffic Application specific”
Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics (2019, background): “2 RELATED WORK There is a large body of work characterizing general Internet traffic [16], [17], [18], [19].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “[69]‡ Supervised NB, Pearson’s χ2 test Proprietary: univ.”