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dc.contributor.authorHsu, Alexander Siruien_US
dc.date.accessioned2019-03-13T08:00:21Z
dc.date.available2019-03-13T08:00:21Z
dc.date.issued2019-03-12
dc.identifier.othervt_gsexam:19033en_US
dc.identifier.urihttp://hdl.handle.net/10919/88421
dc.description.abstractDue to the ever increasing supply of new Internet of Things (IoT) devices being added onto a network, it is vital secure the devices from incoming cyber threats. The manufacturing process of creating and developing a new IoT device allows many new companies to come out with their own device. These devices also increase the network risk because many IoT devices are created without proper security implementation. Utilizing traffic patterns as a method of device type detection will allow behavior identification using only Internet Protocol (IP) header information. The network traffic captured from 20 IoT devices belonging to 4 distinct types (IP camera, on/off switch, motion sensor, and temperature sensor) are generalized and used to identify new devices previously unseen on the network. Our results indicate some categories have patterns that are easier to generalize, while other categories are harder but we are still able recognize some unique characteristics. We also are able to deploy this in a test production network and adapted previous methods to handle streaming traffic and an additional noise categorization capable of identify non-IoT devices. The performance of our model is varied between classes, signifying that much future work has to be done to increase the classification score and overall usefulness.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectMachine Learningen_US
dc.subjectIoTen_US
dc.subjectSmart Devicesen_US
dc.subjectNetwork Trafficen_US
dc.titleAutomatic Internet of Things Device Category Identification using Traffic Ratesen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairTront, Joseph Gen_US
dc.contributor.committeememberButt, Alien_US
dc.contributor.committeememberRaymond, David Richarden_US
dc.contributor.committeememberWang, Gangen_US


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