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Proceedings Paper

Identifying hidden voice and video streams
Author(s): Jieyan Fan; Dapeng Wu; Antonio Nucci; Ram Keralapura; Lixin Gao
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Paper Abstract

Given the rising popularity of voice and video services over the Internet, accurately identifying voice and video traffic that traverse their networks has become a critical task for Internet service providers (ISPs). As the number of proprietary applications that deliver voice and video services to end users increases over time, the search for the one methodology that can accurately detect such services while being application independent still remains open. This problem becomes even more complicated when voice and video service providers like Skype, Microsoft, and Google bundle their voice and video services with other services like file transfer and chat. For example, a bundled Skype session can contain both voice stream and file transfer stream in the same layer-3/layer-4 flow. In this context, traditional techniques to identify voice and video streams do not work. In this paper, we propose a novel self-learning classifier, called VVS-I , that detects the presence of voice and video streams in flows with minimum manual intervention. Our classifier works in two phases: training phase and detection phase. In the training phase, VVS-I first extracts the relevant features, and subsequently constructs a fingerprint of a flow using the power spectral density (PSD) analysis. In the detection phase, it compares the fingerprint of a flow to the existing fingerprints learned during the training phase, and subsequently classifies the flow. Our classifier is not only capable of detecting voice and video streams that are hidden in different flows, but is also capable of detecting different applications (like Skype, MSN, etc.) that generate these voice/video streams. We show that our classifier can achieve close to 100% detection rate while keeping the false positive rate to less that 1%.

Paper Details

Date Published: 13 April 2009
PDF: 12 pages
Proc. SPIE 7344, Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009, 73440F (13 April 2009); doi: 10.1117/12.814986
Show Author Affiliations
Jieyan Fan, Yahoo! Inc. (United States)
Dapeng Wu, Univ. of Florida (United States)
Antonio Nucci, Narus, Inc. (United States)
Ram Keralapura, Narus, Inc. (United States)
Lixin Gao, Univ. of Massachusetts (United States)


Published in SPIE Proceedings Vol. 7344:
Data Mining, Intrusion Detection, Information Security and Assurance, and Data Networks Security 2009
Belur V. Dasarathy, Editor(s)

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