dc.contributor.author	Andrews, Seth Dixon	en
dc.contributor.committeechair	Gerdes, Ryan M.	en
dc.contributor.committeemember	Saad, Walid	en
dc.contributor.committeemember	Li, Ming	en
dc.contributor.committeemember	Reed, Jeffrey H.	en
dc.contributor.department	Electrical Engineering	en
dc.date.accessioned	2019-12-06T09:00:45Z	en
dc.date.available	2019-12-06T09:00:45Z	en
dc.date.issued	2019-12-05	en
dc.description.abstract	Radio frequency fingerprinting, a type of physical layer identification, allows identifying wireless transmitters based on their unique hardware. Every wireless transmitter has slight manufacturing variations and differences due to the layout of components. These are manifested as differences in the signal emitted by the device. A variety of techniques have been proposed for identifying transmitters, at the physical layer, based on these differences. This has been successfully demonstrated on a large variety of transmitters and other devices. However, some situations still pose challenges: Some types of fingerprinting feature are very dependent on the modulated signal, especially features based on the frequency content of a signal. This means that changes in transmitter configuration such as bandwidth or modulation will prevent wireless fingerprinting. Such changes may occur frequently with cognitive radios, and in dynamic spectrum access networks. A method is proposed to transform features to be invariant with respect to changes in transmitter configuration. With the transformed features it is possible to re-identify devices with a high degree of certainty. Next, improving performance with limited data by identifying devices using observations crowdsourced from multiple receivers is examined. Combinations of three types of observations are defined. These are combinations of fingerprinter output, features extracted from multiple signals, and raw observations of multiple signals. Performance is demonstrated, although the best method is dependent on the feature set. Other considerations are considered, including processing power and the amount of data needed. Finally, drift in fingerprinting features caused by changes in temperature is examined. Drift results from gradual changes in the physical layer behavior of transmitters, and can have a substantial negative impact on fingerprinting. Even small changes in temperature are found to cause drift, with the oscillator as the primary source of this drift (and other variation) in the fingerprints used. Various methods are tested to compensate for these changes. It is shown that frequency based features not dependent on the carrier are unaffected by drift, but are not able to distinguish between devices. Several models are examined which can improve performance when drift is present.	en
dc.description.abstractgeneral	Radio frequency fingerprinting allows uniquely identifying a transmitter based on characteristics of the signal it emits. In this dissertation several extensions to current fingerprinting techniques are given. Together, these allow identification of transmitters which have changed the signal sent, identifying using different measurement types, and compensating for variation in a transmitter's behavior due to changes in temperature.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:22201	en
dc.identifier.uri	http://hdl.handle.net/10919/95952	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Device Fingerprinting	en
dc.subject	Physical layer identification	en
dc.subject	transfer learning	en
dc.subject	anomaly detection	en
dc.title	Extensions to Radio Frequency Fingerprinting	en
dc.type	Dissertation	en
thesis.degree.discipline	Electrical Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en

Extensions to Radio Frequency Fingerprinting

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