Defense Notices

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 Previous research on overprivileged permission has become ineffective due to outdated methods and increasing technical complexity.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.

Non-coding RNAs fold into complex 3D structures that govern their biological functions, with RNA structural motifs (RSMs) serving as conserved building blocks of this architecture.
These motifs are defined by characteristic base-interaction patterns, making accurate identification and classification of RNA interactions essential for understanding RNA structure and function.

Despite their biological importance, accurately identifying and classifying these interactions remains challenging because the available data are highly variable in quality and scarce in quantity. This compromises annotation reliability, hinders the construction of trustworthy ground truth for systematic assessment, and restricts the supply of reliable training examples needed for supervised learning.

To address this, we introduce NoBIAS, the first resolution-aware, integrated machine learning-based suite for annotating base interactions from 3D RNA structures, inspired by human pattern recognition, augmented with structure prediction for data enrichment, and evaluated on a carefully curated, stratified benchmark.

NoBIAS is a hierarchical framework for RNA base-interaction annotation that integrates interaction-specific inductive biases with multimodal representation learning. By combining a convolution-augmented, rule-guided module for stacking interactions with complementary graph and image encoders for pairing interactions, NoBIAS captures both structural priors and local visual cues of RNA base doublets. A performance-calibrated logit fusion scheme then adaptively integrates modality-specific predictions based on local-structural resolution, enabling robust inference across heterogeneous 3D RNA structures.

Evaluation across multiple benchmark tiers: spanning consensus, homolog-supported, and manually verified cases, shows that NoBIAS consistently outperforms existing methods under increasingly challenging conditions. Together, the NoBIAS design and its evaluation framework provide a systematic foundation for robust RNA base-interaction annotation, enabling more reliable analysis of RNA structure under realistic uncertainty.

Secure and scalable authentication has become a primary requirement in modern digital ecosystems, where both human biometrics and electronic identities must be verified under noise, large population growth and resource constraints. Existing approaches often struggle to simultaneously provide storage efficiency, dynamic updates and strong authentication reliability. The proposed work advances a unified probabilistic framework based on Hierarchical Bloom Filter (HBF) architectures to address these limitations across biometric and hardware domains. The first contribution establishes the Dynamic Hierarchical Bloom Filter (DHBF) as a noise-tolerant and dynamically updatable authentication structure for large-scale biometrics. Unlike static Bloom-based systems that require reconstruction upon updates, DHBF supports enrollment, querying, insertion and deletion without structural rebuild. Experimental evaluation on 30,000 facial biometric templates demonstrates 100% enrollment and query accuracy, including robust acceptance of noisy biometric inputs while maintaining correct rejection of non-enrolled identities. These results validate that hierarchical probabilistic encoding can preserve both scalability and authentication reliability in practical deployments. Building on this foundation, Bio-BloomChain integrates DHBF into a blockchain-based smart contract framework to provide tamper-evident, privacy-preserving biometric lifecycle management. The system stores only hashed and non-invertible commitments on-chain while maintaining probabilistic verification logic within the contract layer. Large-scale evaluation again reports 100% enrollment, insertion, query and deletion accuracy across 30,000 templates, therefore, solving the existing problem of blockchains being able to authenticate noisy data. Moreover, the deployment analysis shows that execution on Polygon zkEVM reduces operational costs by several orders of magnitude compared to Ethereum, therefore, bringing enrollment and deletion costs below $0.001 per operation which demonstrate the feasibility of scalable blockchain biometric authentication in practice. Finally, the hierarchical probabilistic paradigm is extended to electronic hardware authentication through the Persistent Hierarchical Bloom Filter (PHBF). Applied to electronic fingerprints derived from physical unclonable functions (PUFs), PHBF demonstrates robust authentication under environmental variations such as temperature-induced noise. Experimental results show zero-error operation at the selected decision threshold and substantial system-level improvements as well as over 10^5 faster query processing and significantly reduced storage requirements compared to large scale tracking.

Recent advancements in optical technology are invaluable in a variety of fields, extending far beyond high-speed communications. These innovations enable optical sensing, which plays a critical role across diverse applications, from medical diagnostics to infrastructure monitoring and automotive systems. This research focuses on leveraging commercially available coherent optical transceivers to develop novel measurement techniques to extract detailed information about optical fiber characteristics, as well as target information. Through this approach, we aim to enable accurate and fast assessments of fiber performance and integrity, while exploring the potential for utilizing existing optical communication networks to enhance fiber characterization capabilities. This goal is investigated through three distinct projects: (1) fiber type characterization based on intensity-modulated electrostriction response, (2) coherent Light Detection and Ranging (LiDAR) system for target range and velocity detection through different waveform design, including experimental validation of frequency modulation continuous wave (FMCW) implementations and theoretical analysis of orthogonal frequency division multiplexing (OFDM) based approaches and (3) birefringence measurements using a coherent Polarization-sensitive Optical Frequency Domain Reflectometer (P-OFDR) system.

Electrostriction in an optical fiber is introduced by interaction between the forward propagated optical signal and the acoustic standing waves in the radial direction resonating between the center of the core and the cladding circumference of the fiber. The response of electrostriction is dependent on fiber parameters, especially the mode field radius. We demonstrated a novel technique of identifying fiber types through the measurement of intensity modulation induced electrostriction response. As the spectral envelope of electrostriction induced propagation loss is anti-symmetrical, the signal to noise ratio can be significantly increased by subtracting the measured spectrum from its complex conjugate. We show that if the field distribution of the fiber propagation mode is Gaussian, the envelope of the electrostriction-induced loss spectrum closely follows a Maxwellian distribution whose shape can be specified by a single parameter determined by the mode field radius.        

We also present a self-homodyne FMCW LiDAR system based on a coherent receiver. By using the same linearly chirped waveform for both the LiDAR signal and the local oscillator, the self-homodyne coherent receiver performs frequency de-chirping directly in the photodiodes, significantly simplifying signal processing. As a result, the required receiver bandwidth is much lower than the chirping bandwidth of the signal. Simultaneous multi-target of range and velocity detection is demonstrated experimentally. Furthermore, we explore the use of commercially available coherent transceivers for joint communication and sensing using OFDM waveforms.

In addition, we demonstrate a P-OFDR system utilizing a digital coherent optical transceiver to generate a linear frequency chirp via carrier-suppressed single-sideband modulation. This method ensures linearity in chirping and phase continuity of the optical carrier. The coherent homodyne receiver, incorporating both polarization and phase diversity, recovers the state of polarization (SOP) of the backscattered optical signal along the fiber, mixing with an identically chirped local oscillator. With a spatial resolution of approximately 5 mm, a 26 GHz chirping bandwidth, and a 200 us measurement time, this system enables precise birefringence measurements. By employing three mutually orthogonal SOPs of the launched optical signal, we measure relative birefringence vectors along the fiber.

Accurate biological sequence annotation and privacy-aware clinical modeling are central challenges in modern computational biology and biomedical AI. This dissertation presents scalable and interpretable deep learning frameworks spanning protein family classification, metal-ion binding prediction, and privacy-preserving electrocardiogram (ECG) representation learning. First, we introduce GPCR-SLM, a lightweight transformer-based framework for high-resolution classification of G-protein coupled receptors (GPCRs), one of the largest and most pharmacologically important protein families, targeted by approximately 35% of FDA-approved drugs. Unlike traditional homology-based tools such as BLAST and HMMER, which struggle to distinguish closely related families with low sequence similarity, our knowledge-distilled small language model achieves 99% accuracy across 86 GPCR families. The framework significantly outperforms BLAST (86.4%) and HMMER (91%) while delivering a 33.5× computational speedup compared to large protein language models, enabling scalable functional annotation as protein databases continue to expand. 

Second, we present an end-to-end deep learning pipeline for protein–metal-ion binding prediction. Binding site annotation is traditionally labor-intensive and limited by handcrafted features or predefined residue sets. We systematically evaluate five state-of-the-art protein language models and incorporate positional encoding to capture long-range residue dependencies. Our approach achieves a Matthews Correlation Coefficient (MCC) of 0.89 with precision, recall, and F1 scores exceeding 95% for six major metal ions under 10-fold cross-validation, demonstrating robust predictive performance and improved biological interpretability. Finally, we address fairness and privacy in clinical AI through a variational autoencoder (VAE) framework for ECG representation learning. Because ECGs inherently encode sensitive soft biometrics such as sex, age, and race, we design a dual-discriminator architecture that suppresses demographic information while preserving clinically relevant signals. The reconstructed ECGs substantially reduce demographic identifiability while maintaining strong predictive performance for reduced left ventricular ejection fraction, left ventricular hypertrophy, and 5-year mortality. 

Collectively, this work advances parameter-efficient, scalable, and privacy-conscious deep learning methodologies for both molecular and clinical domains, bridging computational protein science and trustworthy biomedical AI.