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. / Doctor of Philosophy / 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.

In the area of computer and network security, due to the insufficiency, high costs, and user-unfriendliness of existing defending methods against a number of cyber attacks, focus for developing new security improvement methods has shifted from the digital to analog domain. In the analog domain, devices are distinguished based on the present variations and characteristics in their physical signals. In fact, each device has unique features in its signal that can be used for identification and monitoring purposes. In this regard, the term physical layer identification (PLI) or device fingerprinting refers to the process of classifying different electronic devices based on their analog identities that are created by employment of signal processing and data analysis methods. Due to the fact that a device behavior undergoes changes due to variations in external and internal conditions, the available PLI techniques might not be able to identify the device reliably. Therefore, a tracking system that is capable of extracting and explaining the present variations in the electrical signals is required to be developed. In order to achieve the best possible tracking system, a number of prediction models are designed using certain statistical techniques. In order to evaluate the performance of these models, models are run on the acquired data from five different fabrications of the same device in four distinct experiments. The results of performance evaluation show that the surrounding temperature of a device is the best option for predicting its signal. The last part of this research project belongs to the security evaluation of a PLI system. The leveraged security examination technique exposes the PLI system to different types of attacks and evaluates its defending strength accordingly. Based on the mechanism of the employed attack in this work, the forged version of a device’s signal is generated using an arbitrary waveform generator (AWG) and is sent to the PLI system. The outcomes of this experiment indicate that the leveraged PLI technique is strong enough in defeating this attack.