I2S Masters/ Doctoral Theses

Modern intelligent systems increasingly rely on continuous sensor data streams for perception, decision-making, and control, making sensors a critical yet underexplored attack surface. While prior research has demonstrated the feasibility of sensor-based attacks, recent advances in mobile operating systems and machine learning-based defenses have significantly reduced their practicality, rendering them more detectable, resource-intensive, and constrained by evolving permission and context-aware security models.

This dissertation revisits sensor exploitation under these modern constraints and develops a unified, cross-layer perspective that improves both practicality and stealth of sensor-enabled attacks. We identify three fundamental challenges: (i) the difficulty of reliably manipulating physical sensor signals in noisy, real-world environments; (ii) the effectiveness of context-aware defenses in detecting anomalous sensor behavior on mobile devices, and (iii) the lack of lightweight coordination for practical sensor-based side- and covert-channels.

To address the first challenge, we propose a physical-domain attack framework that integrates signal modeling, simulation-guided attack synthesis, and real-time adaptive targeting, enabling robust adversarial perturbations with high attack success rates even under environmental uncertainty. As a case study, we demonstrate an infrared laser-based adversarial example attack against face recognition systems, which achieves consistently high success rates across diverse conditions with practical execution overhead.

To improve attack stealth against context-aware defenses, we introduce an auto-contextualization mechanism that synchronizes malicious sensor actuation with legitimate application activity. By aligning injected signals with both statistical patterns and semantic context of benign behavior, the approach renders attacks indistinguishable from normal system operations and benign sensor usage. We validate this design using three Android logic bombs, showing that auto-contextualized triggers can evade both rule-based and learning-based detection mechanisms.

Finally, we extend sensor exploitation beyond the traditional attack-channel plane by introducing a lightweight control-plane protocol embedded within sensor data streams. This protocol encodes control signals directly into sensor observations and leverages simple signal-processing primitives to coordinate multi-stage attacks without relying on privileged APls or explicit inter-process communication. The resulting design enables low-overhead, stealthy coordination of cross-device side- and covert-channels.

Together, these contributions establish a new paradigm for sensor exploitation that spans physical, contextual, and control-plane dimensions. By bridging these layers, this dissertation demonstrates that sensor-based attacks remain not only feasible but also practical and stealthy in modern computer systems.

Modern phased-array antenna calibration is essential for advanced radar systems to achieve precise beamforming, sidelobe control, and coherent processing. While mutual coupling-based calibration provides a valuable internal alternative to external far-field references by exploiting near-field element interactions, the problem is fundamentally ill-posed. Measured responses depend simultaneously on transmit coefficients, receive coefficients, and the coupling matrix, making it difficult to isolate true channel errors from array-model mismatch without additional structure.

This thesis presents a Bayesian Maximum A Posteriori (MAP) calibration framework that resolves this ambiguity by embedding physically motivated prior information into the estimation problem. The nominal coupling matrix is decomposed into Infinite, Symmetric, and Reciprocal components, which define low-dimensional parameterizations and prior covariance models. A Maximum Likelihood (ML) stage first generates a data-consistent transceiver initialization, followed by a MAP estimator that refines the solution by jointly addressing structured coupling deviations and measurement uncertainty.

Evaluations using Computational Electromagnetic (CEM) models and measured WaDES array data reveal that the physical array contains more higher-order structural content than the nominal CEM model. Across Monte Carlo trials, highly structured MAP estimators generally achieve lower aggregate error than unconstrained ML and Log Least Squares (LLS) methods. The overlapping-subspace M family offers an optimal balance of structural flexibility, zero-centered phase and magnitude behavior, and tuning robustness. Additionally, parametric sweeps highlight that prior covariance scaling is a critical design parameter: tight reciprocal priors prevent spurious structural absorption, whereas overly loose priors allow model mismatch to contaminate transceiver estimates.

Ultimately, this work demonstrates that internal mutual coupling calibration can achieve autonomy and robustness against model mismatch by parameterizing the nominal coupling matrix into structured components and integrating them as Bayesian priors.

Substance use remains a major public health issue in the United States that significantly impacts individuals, families, and society. Many individuals who suffer from substance use disorder (SUD) face incarceration due to drug-related offenses. Drug courts have emerged as an alternative to imprisonment and offer the opportunity for individuals to participate in a drug rehabilitation program instead. Drug courts mainly focus on those with non-violent drug-related offenses. One of the challenges of decision making in drug courts is assessing the likelihood of participants graduating from the drug court and avoiding recidivism after graduation. This study investigates the use of machine learning models to predict success in drug courts using data from a substance use drug court in Missouri. Success is measured in terms of graduation from the program, and the model includes a wide range of potential predictors including demographic characteristics, family and social factors, substance use history, legal involvement, physical and mental health history, employment history as well as drug court participation data. The results will be beneficial to drug court teams and presiding judges in predicting client success, evaluating risk factors during treatment for participants, informing person-centered treatment planning, and the development of after-care plans for high-risk participants to reduce the likelihood of recidivism. 

Optical Neural Networks (ONNs) have gained traction as an alternative to the conventional computing architectures used in modern CPUs and GPUs, largely because light enables massive parallelism, ultrafast inference, and minimal power consumption. 

As with conventional deep neural networks (DNNs), free-space ONNs require two main layers: (1) a nonlinear activation function which exists to separate adjacent linear layers, and (2) weighting layers that applies a linear transformation given an input.

Firstly, a Resonant Semiconductor Saturable Absorption Mirror (RSAM) was investigated as a viable nonlinear activation function. Several mechanisms have been used to create nonlinear activation functions, such as cold atoms, vapor absorption cells, and polaritons, but these implementations are bulky and must operate under tightly controlled environments while RSAMs is a passive device. Compared to typical SESAMs, the resonance structure of RSAM also reduces the saturation fluence compared to non-resonant SAMs, allowing low power laser sources to be used. A fiber-based optical testbed demonstrated notable improvement of 8.1% in classification accuracy compared to a linear only network trained with the MNIST dataset.

Secondly, Micro-electromechanical-system-based phase light modulators (PLMs) were evaluated as an alternative to LC-SLMs for in-situ reinforcement learning. PLMs can operate at kilohertz-scale frame rates at a substantially lower cost compared to LC-SLMs but have lower phase resolution and non-uniform quantization which impacts fidelity. Despite these disadvantages, the high-speed nature of PLMs allows for significant decrease in optimization time, which not only allows for reduction in training time, but also allows for larger datasets and more complex models with more learnable parameters. A single layer optical network was implemented using policy-based learning with discrete action-space to minimize impact of quantization. The testbed achieves 90.1%, 79.7%, and 76.9% training, validation, and test accuracy, respectively, on 3,000 images from the MNIST dataset. Additionally, we achieved 79.9%, 72.1%, and 71.7% accuracy on 3,000 images from the Fashion MNIST dataset. At 14 minutes per epoch during training, it is at least a magnitude lower in training time compared to LC-SLMs based models.

Checkpointing strategies in high-performance computing traditionally employ the Young-Daly formula to determine the (first-order) optimal duration between checkpoints, which assumes a known mean time between faults (MTBF). However, in practice, the MTBF may not be known accurately or may vary, causing Young-Daly checkpointing to perform sub-optimally. In 2021, Sigdel et al. introduced the CHORE (CHeckpointing Overhead and Rework Equated) checkpointing strategy, which is MTBF-agnostic yet demonstrates a bounded increase in overhead compared to the optimal strategy. This thesis analyzed and extends the CHORE framework in several ways. First, it verifies Sigdel et al.’s claims about the relative overhead of the CHORE strategy through both event-driven simulations and expected runtimes derived from the underlying probablistic model. Second, it extends the CHORE strategy to silent errors, which must be deliberately checked for to be detected. In this scenario, the overhead compared to optimal checkpointing is once more analyzed through simulations and expected runtimes. Third, a heuristic is proposed to offer improved performance of the CHORE algorithm under typical runtime scenarios by interpreting CHORE as an additive-increase multiplicative-decrease model and tuning the parameters.

Ultrawideband (UWB) Interferometric Synthetic Aperture Radar (InSAR) integrated with multi-rotor Uncrewed Aerial Vehicle (UAV), or UIMU in this work for brevity, provides ultrafine-resolution, all-weather, 3D surface imagery at any time of day. UIMU can be rapidly deployable and low-cost, and therefore a critical new tool for low-altitude remote sensing applications, such as disaster response, environmental monitoring, and intelligence surveillance and reconnaissance (ISR). Traditional repeat-pass data collection methods reduce the phase coherence required for InSAR processing of ultrafine-resolution datasets due to the unstable flight behavior of multi-rotor UAVs. Collecting Synthetic Aperture Radar (SAR) datasets using two receive channels during a single-pass will improve phase coherence and the ability to produce ultrafine-resolution 3D InSAR imagery.

This work proposes to quantify and characterize 3D target-position accuracy for a dual-channel 6 GHz bandwidth (2 cm range resolution) frequency modulated continuous wave (FMCW) radar integrated with the Aurela X6 hexacopter to establish novel single-pass UWB InSAR data collection methods and processing algorithms for multi-rotor UAV. The feasibility of the proposed investigation is demonstrated by the preliminary qualitative analysis of single-pass InSAR imagery presented in this proposal. Fieldwork will be conducted to measure the positions of GPS located corner reflectors using the UIMU system. Algorithms for motion tolerant Time-Domain Backprojection (TDBP), InSAR coregistration, and digital elevation mapping novel to multi-rotor UAV at UWB will be developed and presented. An analysis of vehicle motion induced phase decoherence, and InSAR imagery signal to noise ratio (SNR) will be presented. The TDBP SNR performance will be compared to the Open Polar Radar Omega-K algorithm to attempt to quantify motion tolerance between the different SAR processing algorithms.

This work will establish a foundation for future investigations of real-time image processing, separated transmission and receive platforms (bistatic), or swarm configurations for UIMU systems.

Graph Neural Networks (GNNs) achieve state-of-the-art performance in modeling complex biological and behavioral systems, yet their "black-box" nature limits their utility for scientific discovery and clinical translation. Standard post-hoc explainability methods typically attribute importance to low-level features, such as individual nodes or edges, which often fail to map onto the high-level, domain-specific concepts utilized by experts. To address this gap, this thesis explores diverse methodological strategies for achieving Concept-Level Interpretability in GNNs, demonstrating how deep learning models can be structurally and analytically aligned with expert domain knowledge. This theme is explored through two distinct methodological paradigms applied to critical challenges in neuroscience and clinical psychology. First, we introduce an interpretable-by-design approach for modeling brain structure-function coupling. By employing an ensemble of GNNs conceptually biased via input graph filtering, the model enforces verifiably disentangled node embeddings. This allows for the quantitative testing of specific structural hypotheses, revealing that a minority of strong anatomical connections disproportionately drives functional connectivity predictions. Second, we present a post-hoc conceptual alignment paradigm for quantifying atypical motor signatures in Autism Spectrum Disorder (ASD). Utilizing a Spatio-Temporal Graph Autoencoder (STGCN-AE) trained on normative skeletal data, we establish an unsupervised anomaly detection system. To provide clinical interpretability, the model's reconstruction error is systematically aligned with a library of human-interpretable kinematic features, such as postural sway and limb jerk. Explanatory meta-modeling via XGBoost and SHAP analysis further translates this abstract loss into a multidimensional clinical signature. Together, these applications demonstrate that integrating concept-level interpretability through either architectural design or systematic post-hoc alignment enables GNNs to serve as robust tools for hypothesis testing and clinical assessment.

The adoption of Commercial off-the-shelf (COTS) components has become a dominant paradigm in modern system design due to their reduced development cost, faster time-to-market, and widespread availability. However, the reliance on globally distributed and untrusted supply chains introduces significant security risks, particularly the possibility of malicious hardware modifications such as Trojans, embedded during design or fabrication. In such settings, traditional methods that depend on golden models, full design visibility, or trusted fabrication are no longer sufficient, creating the need for new security assurance approaches under a zero-trust model. This proposed research addresses security challenges in COTS microprocessors through two complementary solutions: runtime resilience and pre-deployment trust verification. First, a multi-variant-execution-based framework is developed that leverages functionally equivalent program variants to induce diverse microarchitectural execution patterns. By comparing intermediate outputs across variants, the framework enables runtime detection and tolerance of Trojan induced payload effects without requiring hardware redundancy or architectural modifications. To enhance the effectiveness of variant generation, a reinforcement learning assisted framework is introduced, in which the reward function is defined by security objectives rather than traditional performance optimization, enabling the generation of variants that are more robust against repeated Trojan activation. Second, to enable black-box trust verification prior to deployment, this work presents a framework that can efficiently test the presence of hardware Trojans by identifying microarchitectural rare events and transferring activation knowledge from existing processor designs to trigger highly susceptible internal nodes. By leveraging ISA-level knowledge, open-source RTL references, and LLM-guided test generation, the framework achieves high trigger coverage without requiring access to proprietary designs or golden references. Building on these two scenarios, a future research direction is outlined for evolving trust in COTS hardware through continuous runtime observation, where multi-variant execution is extended with lightweight monitoring mechanisms that capture key microarchitectural events and execution traces. These observations are accumulated as hardware trust counters, enabling the system to progressively establish confidence in the underlying hardware by verifying consistent behavior across diverse execution patterns over time. Together, these directions establish a foundation for analyzing and mitigating security risks across zero-trust COTS supply chains.

Android is the most widely adopted mobile operating system, supporting billions of devices and driven by a robust app ecosystem.  Its permission-based security model aims to enforce the Principle of Least Privilege (PoLP), restricting apps to only the permissions it needs.  However, many apps still request excessive permissions, increasing the risk of data leakage and malicious exploitation. Previous research on overprivileged permission has become ineffective due to outdated methods and increasing technical complexity.  The introduction of runtime permissions and scoped storage has made some of the traditional analysis techniques obsolete.  Additionally, developers often are not transparent in explaining the usage of app permissions on the Play Store, misleading users unknowingly and unwillingly granting unnecessary permissions. This combination of overprivilege and poor transparency poses significant security threats to Android users.  Recently, the rise of local large language models (LLMs) has shown promise in various security fields. The main focus of this study is to analyze whether an app is overpriviledged based on app description provided on the Play Store using Local LLM. Finally, we conduct a manual evaluation to validate the LLM’s findings, comparing its results against human-verified response.

Electronic Health Records (EHRs) provide rich longitudinal patient information, but their high dimensionality, sparsity, heterogeneity, and temporal complexity make robust representation learning difficult. This dissertation studies how to improve patient and medical concept representation learning in EHRs and consequently enhance healthcare predictive tasks by integrating domain knowledge, knowledge graphs, large language models (LLMs), and contrastive learning. First, it introduces an ontology-aware temporal contrastive framework for survival analysis that learns discriminative patient representations from censored and observed trajectories by modeling temporal distinctiveness in longitudinal EHR data. Second, it proposes a multi-ontology representation learning framework that jointly propagates knowledge within and across diagnosis, medication, and procedure ontologies, enabling richer medical concept embeddings, especially under limited data and for rare conditions. Third, it develops an LLM-enriched, text-attributed medical knowledge graph framework that combines EHR-derived statistical evidence with type-constrained LLM reasoning to infer semantic relations, generate contextual node and edge descriptions, and co-learn concept embeddings through joint language-model and graph-neural-network training. Together, these studies advance a unified view of EHR representation learning in which structured medical knowledge, textual semantics, and temporal patient trajectories are jointly leveraged to build more accurate, interpretable, and robust healthcare prediction models.