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On Improved Generalization of 5-State Hidden Markov Model-based Internet Traffic Classifiers

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dc.contributor.advisor Kremer, Stefan
dc.contributor.author Bartnik, Grant
dc.date.accessioned 2013-06-06T19:40:33Z
dc.date.available 2013-06-06T19:40:33Z
dc.date.copyright 2013-05
dc.date.created 2013-05-17
dc.date.issued 2013-06-06
dc.identifier.uri http://hdl.handle.net/10214/7237
dc.description.abstract The multitude of services delivered over the Internet would have been difficult to fathom 40 years ago when much of the initial design was being undertaken. As a consequence, the resulting architecture did not make provisions for differentiating between, and managing the potentially conflicting requirements of different types of services such as real-time voice communication and peer-to-peer file sharing. This shortcoming has resulted in a situation whereby services with conflicting requirements often interfere with each other and ultimately decrease the effectiveness of the Internet as an enabler of new and transformative services. The ability to passively identify different types of Internet traffic then would address this shortcoming and enable effective management of conflicting types of services, in addition to facilitating a better understanding of how the Internet is used in general. Recent attempts at developing such techniques have shown promising results in simulation environments but perform considerably worse when deployed into real-world scenarios. One possible reason for this descrepancy can be attributed to the implicit assumption shared by recent approaches regarding the degree of similarity between the many networks which comprise the Internet. This thesis quantifies the degradation in performance which can be expected when such an assumption is violated as well as demonstrating alternative classification techniques which are less sensitive to such violations. en_US
dc.language.iso en en_US
dc.rights Attribution 2.5 Canada *
dc.rights.uri http://creativecommons.org/licenses/by/2.5/ca/ *
dc.subject Hidden Markov Model en_US
dc.subject Internet en_US
dc.subject Quality of Service en_US
dc.subject Hypothesis Test en_US
dc.subject Machine Learning en_US
dc.subject Classification en_US
dc.title On Improved Generalization of 5-State Hidden Markov Model-based Internet Traffic Classifiers en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US


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Attribution 2.5 Canada Except where otherwise noted, this item's license is described as Attribution 2.5 Canada