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A PRELIMINARY STUDY FOR IDENTIFYING NAT TRAFFIC USING MACHINE LEARNING

dc.contributor.authorGokcen, Yasemin
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorDr. Evangelos E. Miliosen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Malcolm Heywooden_US
dc.contributor.thesis-readerDr. Srinivas Sampallien_US
dc.contributor.thesis-supervisorDr. Nur Zincir-Heywooden_US
dc.date.accessioned2014-04-07T11:53:31Z
dc.date.available2014-04-07T11:53:31Z
dc.date.defence2014-04-01
dc.date.issued2014-04-07
dc.description.abstractIt is shown in the literature that the NAT devices have become a convenient way to hide the identity of malicious behaviors. In this thesis, the aim is to identify the presence of the NAT devices in the network traffic and (if possible) to predict the number of users behind those NAT devices. To this end, I utilize different approaches and evaluate the performance of these approaches under different network environments represented by the availability of different data fields. To achieve this, I propose a machine learning (ML) based approach to detect NAT devices. I evaluate my approach against different passive fingerprinting techniques representing the state-of-the-art in the literature and show that the performance of the proposed ML based approach is very promising even without using any payload (application layer) information.en_US
dc.identifier.urihttp://hdl.handle.net/10222/49104
dc.language.isoenen_US
dc.subjectNetwork Address Translation Classificationen_US
dc.subjectTraffic Flowsen_US
dc.subjectTraffic Analysisen_US
dc.subjectMachine Learningen_US
dc.subjectForensic Analysisen_US
dc.titleA PRELIMINARY STUDY FOR IDENTIFYING NAT TRAFFIC USING MACHINE LEARNINGen_US

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