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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.

WiFi networks are susceptible to statistical traffic analysis attacks. Despite encryption, the metadata of encrypted traffic, such as packet inter-arrival time and size, remains visible. This visibility allows potential eavesdroppers to infer private information in the Internet of Things (IoT) environment. For example, it allows for the identification of sleep monitors and the inference of whether a user is awake or asleep. WiFi eavesdropping theoretically enables the identification of IoT devices without the need to join the victim's network. This attack scenario is more realistic and much harder to defend against, thus posing a real threat to user privacy. However, researchers have not thoroughly investigated this type of attack due to the noisy nature of wireless channels and the relatively low accuracy of WiFi sniffers. Furthermore, many countermeasures proposed in the literature are inefficient in addressing side-channel leakage in WiFi networks. They often burden internet traffic with high data overhead and disrupt the user experience by introducing deliberate delays in packet transmission. This dissertation investigates privacy leakage resulting from WiFi eavesdropping and proposes efficient defensive techniques. We begin by assessing the practical feasibility of IoT device identification in WiFi networks. We demonstrate how an eavesdropper can fingerprint IoT devices by passively monitoring the wireless channel without joining the network. After exploring this privacy attack, we introduce a traffic spoofing-based defense within the WiFi channel to protect against such threats. Additionally, we propose a more data-efficient obfuscation technique to counter traffic analytics based on packet size without adding unnecessary noise to the traffic.

Abstract—This dissertation investigates the privacy implications of WiFi communication in Internet-of-Things (IoT) environments, focusing on the threat posed by out-of-network observers. Recent research has shown that in-network observers can glean information about IoT devices, user identities, and activities. However, the potential for information inference by out-of-network observers, who do not have WiFi network access, has not been thoroughly examined. The first study provides a detailed summary dataset, utilizing Random Forest for data summary classification. This study highlights the significant privacy threat to WiFi networks and IoT applications from out-of-network observers. Building on this investigation, the second study extends the research by utilizing a new set of time series monitored WiFi data frames and advanced machine learning algorithms, specifically xGboost, for Time Series classification. This extension achieved high accuracy of up to 94\% in identifying IoT devices and their working status, demonstrating faster IoT device profiling while maintaining classification accuracy. Furthermore, the study underscores the ease with which outside intruders can harm IoT devices without joining a WiFi network, launching attacks quickly and leaving no detectable footprints. Additionally, the dissertation presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. Given the challenges posed by encryption for traditional packet and traffic inspection, understanding and classifying encrypted traffic are crucial. The survey provides insights into utilizing machine learning for encrypted network traffic analysis and classification, reviewing state-of-the-art techniques and methodologies. This survey serves as a valuable resource for network administrators, cybersecurity professionals, and policy enforcement entities, offering insights into current practices and future directions in encrypted traffic analysis and classification.

Chiplets have become an increasingly popular technology for extending Moore's Law and improving the reliability of integrated circuits. They do this by placing several small, interacting chips on an interposer rather than the traditional, single chip used for a device. Like any other type of integrated circuit, chiplets are in need of a physical layer of security to defend against hardware Trojans, counterfeiting, probing, and other methods of tampering and physical attacks. Power distribution networks are ubiquitous across chiplet and monolithic ICs, and are essential to the function of the device. Thus, we propose a method of fingerprinting transient signals within the PDN to identify individual chiplet systems and physical-layer threats against these devices. In this work, we describe a Python-wrapped HSPICE model we have built to automate testing of our proposed PDN fingerprinting methods. We also document the methods of analysis used- wavelet transforms and time-domain measurements- to identify unique characteristics in the voltage response signals to transient stimuli. We provide the true positive and false positive rates of these methods for a simulated lineup of chips across varying operating conditions to determine uniqueness and reliability of our techniques. Our simulations show that, if characterized at varying supply voltage and temperature conditions in the factory, and the sensors used for identification meet the sample rates and voltage resolutions used in our tests, our protocol provides sufficient uniqueness and reliability to be enrolled. We recommend that experimentation be done to evaluate our methods in hardware and implement sensing techniques to meet the requirements shown in this work.

The primary objective of the proposed research is to develop a framework for smart and robust fingerprinting of networked systems. Many fingerprinting techniques have been proposed in the past, however most of these techniques are designed for a specific purpose, such as Operating System (OS) fingerprinting, Access Point (AP) fingerprinting, etc. Such standalone techniques often have limitations which render them dysfunctional in certain scenarios or against certain counter measures. In order to overcome such limitations, we propose a fingerprinting framework that can combine multiple fingerprinting techniques in a smart manner, using a centralized decision making engine. We believe that any given scenario or a counter measure is less likely to circumvent a group of diverse fingerprinting techniques, which serves as the primary motivation behind the aforementioned method of attack. Another major portion of the thesis concentrates on the design and development of a device and device type fingerprinting sub-module (GTID) that has been integrated into the proposed framework. This sub-module used statistical analysis of packet inter arrival times (IATs) to identify the type of device that is generating the traffic. This work also analyzes the performance of the identification technique on a real campus network and propose modifications that use pattern recognition neural networks to improve the overall performance. Additionally, we impart capabilities to the fingerprinting technique to enable the identification of 'Unknown' devices (i.e., devices for which no signature is stored), and also show that it can be extended to perform both device and device type identification.