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dc.contributor.authorMeyer, Carl Brett
dc.date.accessioned2014-03-04T20:00:03Z
dc.date.available2014-03-04T20:00:03Z
dc.date.issued2011-05
dc.identifier.othermeyer_carl_b_201105_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/meyer_carl_b_201105_ms
dc.identifier.urihttp://hdl.handle.net/10724/27231
dc.description.abstractPeer-to-Peer botnets are of particular concern in the world of network security because of the difficulty involved in identifying the botmaster node in the network. This paper seeks to address this issue incrementally by developing a statistical model for the control, or signaling, network flows of the most popular P2P VoIP application, Skype, as a first step toward identifying known P2P applications for the purposes of whitelisting them in a network trace. Through construction of a dataset containing real-world Skype traces and real-world traces for four other popular P2P file-sharing programs, a statistical model is created which incorportates the flow behaviors of Skype control flows. This statistical model is tested using four classification algorithms, and the results show a very high accuracy and low false positive rate for successfully identifying Skype control flows against the control flows of the other P2P applications.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectSkype
dc.subjectsupervised learning
dc.subjectmachine learning
dc.subjectnetwork security
dc.subjectbotnet
dc.titleCalling all nodes
dc.title.alternativeclassifying Skype overlay control flows
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKang Li
dc.description.committeeKang Li
dc.description.committeeKhaled Rasheed
dc.description.committeeRoberto Perdisci


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