Extensions to Radio Frequency Fingerprinting

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• Andrews, Seth Dixon

Chair dc:contributor.committeechair

• Gerdes, Ryan M.

Committee members dc:contributor.committeemember

• Saad, Walid
• Li, Ming
• Reed, Jeffrey H.

• Device Fingerprinting
• Physical layer identification
• transfer learning
• anomaly detection

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:22201

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/95952