Defense Notices

Checkpointing strategies in high-performance computing traditionally employ the Young-Daly for-

mula 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 op-

timal 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 ana-

lyzed 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.

Individuals with Autism Spectrum Disorder (ASD) frequently experience elevated stress and are at higher risk for mood disorders such as anxiety and depression. Sensory over-responsivity, social challenges, and difficulties with emotional recognition and regulation contribute to such heightened stress. This study presents a proof-of-concept system that detects and mitigates stress through interactions with a virtual pet. Designed for young adults with high-functioning autism, and potentially useful for people beyond that group, the system monitors simulated heart rate, skin resistance, body temperature, and environmental sound and light levels. Upon detection of stress or potential triggers, the system alerts the user and offers stress-reduction activities via a virtual pet, including guided deep-breathing exercises and interactive engagement with the virtual companion. Through combining real-time stress detection with interactive interventions on a single platform, the system aims to help autistic individuals recognize and manage stress more effectively.

Facial recognition systems increasingly rely on machine learning services, yet they remain vulnerable to cyber-attacks. While traditional adversarial attacks target input data, an underexplored threat comes from weight manipulation attacks, which directly modify model parameters and can compromise deployed systems in cyber-physical settings. This paper investigates defenses against Weight Surgery, a weight manipulation attack that modifies the final linear layer of neural networks to merge or shatter classes without requiring access to training data. We propose a computationally lightweight defense capable of detecting sample pairs affected by Weight Surgery at low false-positive rates. The defense is designed to operate in realistic deployment scenarios, selecting its sensitivity parameter 𝛾 using only benign samples to meet a target false-positive rate. Evaluation on 1000 independently attacked models demonstrates that our method achieves over 95% recall at a target false-positive rate of 0.001. Performance remains strong even under stricter conditions: at FPR = 0.0001, recall is 92.5%, and at 𝛾=0.98, FPR drops to 0.00001 while maintaining 88.9% recall. These results highlight the robustness and practicality of the defense, offering an effective safeguard for neural networks against model-targeted attacks.

Microelectronics supply chains increasingly rely on globally distributed design, fabrication, integration, and deployment processes, making traditional assumptions of trusted hardware inadequate. Security in this setting can be understood through a zero-trust microelectronics supply-chain model, in which neither manufacturing partners nor procured hardware platforms are assumed trustworthy by default. Two complementary threat scenarios are considered in the proposed research. In the first scenario, custom Integrated Circuits (ICs) fabricated through potentially untrusted foundries are examined, where design-for-security protections intended to prevent piracy, overproduction, and intellectual-property theft can themselves become vulnerable to attacks. In this scenario, hardware Trojan-assisted meta-attacks are used to show that such protections can be systematically identified and subverted by fabrication-stage adversaries. In the second scenario, commercial off-the-shelf ICs are considered from the perspective of end users and procurers, where internal design visibility is unavailable and hardware trustworthiness cannot be directly verified. For this setting, runtime-oriented protection mechanisms are developed to safeguard sensitive computation against malicious hardware behavior and side-channel leakage. Building on these two scenarios, a future research direction is outlined for side-channel-driven vulnerability discovery in off-the-shelf devices, motivated by the need to evaluate and test such platforms prior to deployment when no design information is available. The proposed direction explores gray-box security evaluation using power and electromagnetic side-channel analysis to identify anomalous behaviors and potential vulnerabilities in opaque hardware platforms. Together, these directions establish a foundation for analyzing and mitigating security risks across zero-trust microelectronics supply chains.

Many High Performance Computing (HPC) applications following the Bulk Synchronous Parallel

(BSP) model are increasingly deployed in cloud-native, multi-tenant container environments such

as Kubernetes. Unlike dedicated HPC clusters, these shared platforms introduce resource virtualization

and variability, making BSP applications more susceptible to performance fluctuations.

Workload imbalance across supersteps can trigger the straggler effect, where faster tasks wait

at synchronization barriers for slower ones, increasing overall execution time. Existing BSP resource

management approaches typically assume static workloads and reuse a single configuration

throughout execution. However, real-world workloads vary due to dynamic data and system conditions,

making static configurations suboptimal. This limitation underscores the need for adaptive

resource management strategies that respond to workload changes while considering reconfiguration

costs.

To address these limitations, we evaluate a dynamic, data-driven resource management framework

tailored for cloud-native BSP applications. This approach integrates workload profiling,

time-series forecasting, and predictive performance modeling to estimate task execution behavior

under varying workload and resource conditions. The framework explicitly models the trade-off

between performance gains achieved through reconfiguration and the associated checkpointing

and migration costs incurred during container reallocation. Multiple reconfiguration strategies

are evaluated, spanning simple window-based heuristics, dynamic programming methods, and

reinforcement learning approaches. Through extensive experimental evaluation, this framework

demonstrates up to 24.5% improvement in total execution time compared to a baseline static configuration.

Furthermore, we systematically analyze the performance of each strategy under varying

workload characteristics, simulation lengths, and checkpoint penalties, and provide guidance on

selecting the most appropriate strategy for a given workload environment.