Computer Sciences Commons^™

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Sciteuq: Toward Uncertainty-Aware Complex Scientific Table Data Extraction And Understanding, Kehinde Ajayi

Computer Science Theses & Dissertations

Scientific tables report critical research insights, data, and findings for scientific progress. Because Portable Document Format (PDF) is the de facto standard format for scientific paper publishing, there has been an emerging need for an automatic method to extract data from PDF files. A significant fraction of scientific tables exhibit complex structure and content, making it challenging for machine learning tools to accurately extract the content directly from PDF files. Despite the advancements in Table Structure Recognition (TSR), automated extraction of data from complex scientific tables remains a challenge due to variations in table structures and contents. In this dissertation, …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Making Explanations Make Sense: Xai For Smishing Detection, Eleni Alexandra Katsarakes

Psychology Theses & Dissertations

Explainable Artificial Intelligence (XAI) is a key component of effective human-AI collaboration, particularly in high-stakes domains such as cybersecurity. While AI tools hold promise for mitigating threats such as SMS-based phishing (SMiShing), their real-world effectiveness may hinge not just on detection accuracy, but on whether users can make sense of the system’s outputs. As SMiShing attacks grow in both frequency and sophistication, so does the urgency of designing human-centered AI systems that support user decision-making under uncertainty. This study examined how four distinct AI explanation types - Normative (rule-based), Attributive (feature-based), Exemplar (case-based), and Recommendation-Only - influence user performance, confidence, …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

Toward Personalizing Quantum Computing Education: An Evolutionary Llm-Powered Approach, Iizalaarab Elhaimeur

Computer Science Theses & Dissertations

Quantum computing education faces significant challenges due to its complexity and the limitations of current tools. This thesis introduces a novel Intelligent Teaching Assistant for quantum computing education and details its evolutionary design process. The system combines a knowledge-graph-augmented architecture with two specialized LLM agents: a Teaching Agent for dynamic interaction and a Lesson Planning Agent for lesson generation. The system is designed to adapt to individual student needs, with interactions meticulously tracked and stored in a knowledge graph. This graph represents student actions, learning resources, and their relationships, aiming to enable reasoning about effective learning pathways. We describe the …

Insider Threat: A Case Study Of The Maroochy Water Services Attack, Samuel Rector

Cybersecurity Undergraduate Research Showcase

In 2000, a former employee at Hunter Watertech went rogue and caused a spill of 800,000 liters of sewage. He leveraged his insider knowledge and access to stolen equipment in order to seek retribution for the company that wronged him. After three months of torment police arrested him and an investigation and many studies were done on the incident. A consensus remains that much of this chaos was preventable with simple cybersecurity implementations.

Behavioral Detection Methods For Automated Mcp Server Vulnerability Assessment, Christian Coleman

Cybersecurity Undergraduate Research Showcase

The Model Context Protocol (MCP) has emerged as a critical standard for connecting AI agents to external data sources and tools. Still, its adoption has introduced significant security vulnerabilities across multiple attack surfaces. While recent research has catalogued extensive vulnerability taxonomies and attack implementations, automated detection methodologies remain limited. Current detection tools primarily employ static code analysis, which fails to identify behavioral vulnerabilities that only manifest during runtime server interactions. This study explores behavioral detection approaches for identifying MCP server vulnerabilities through systematic query-based testing, with particular emphasis on context manipulation techniques. Preliminary analysis of existing vulnerability research reveals 48 …

Game Hacking & Anti-Cheat Analysis, Quang Hoang

Cybersecurity Undergraduate Research Showcase

Reverse engineering and analyzing game hacking and anti-cheat mechanisms is a complex and evolving field. This research document explores the history of game hacking, various techniques used in game hacking, and the countermeasures implemented by anti-cheat systems. Through case studies, we illustrate the strategies involved in creating cheats. Specifically, we demonstrate how to hack the open-source game AssaultCube using memory editing and code injection techniques available in Cheat Engine in a step-by-step manner so that the reader can theoretically reproduce the results shown in this paper. We also dissect the game’s anti-cheat mechanisms, identifying their strengths and weaknesses. This document …

A Scalable Cybersecurity Model For Academic Makerspaces, William Faircloth

Cybersecurity Undergraduate Research Showcase

Academic makerspaces have become integral hubs of innovation on university campuses, providing students with access to industrial-grade operational technology (OT) such as 3D printers and CNC machines. However, the security posture for these spaces has overwhelmingly focused on physical safety, creating a significant cybersecurity gap. This oversight leaves networked OT vulnerable to cyberattacks, which threaten student intellectual property, expensive equipment, and the integrity of the broader institutional network. This research addresses this critical vulnerability by developing and implementing a secure and scalable cybersecurity model at the Old Dominion University Computer Science Makerspace, founded on two core principles: robust network segmentation …

Deconstructing Tycoon 2fa: A Static Analysis Approach To Threat Intelligence And Automated Defense, Daniel A. Austin Jr

Cybersecurity Undergraduate Research Showcase

It's gotten much easier to be a cybercriminal. We're seeing a boom in "Phishing-as-a-Service" (PaaS) platforms, which sell advanced phishing attacks as a ready-to-use product. This means almost anyone can now get the tools to launch sophisticated attacks, even if they don't have a lot of technical skill.

This research dives into one of the most prominent threats, the Tycoon 2FA phishing kit. This kit is dangerous because it's designed to bypass Multi-Factor Authentication (MFA) using what is known as an Adversary-in-the-Middle (AiTM) attack.

This paper covers how I built and tested a set of Python-based tools to perform "static …

A Systematic Review Of Poisoning Attacks Against Large Language Models (Llm), Patrick Mcguffin

Cybersecurity Undergraduate Research Showcase

This paper provides a comprehensive review of poisoning attacks against large language models (LLMs), drawing primarily from Fendley et al. (2025) and complementary studies from 2022–2025. It categorizes poisoning research into two key dimensions, Metrics and Specifications, to evaluate how attack success is measured and how attacks are implemented. This paper synthesizes quantitative results, experimental findings, and defense strategies across data, model, and multi-modal poisoning contexts. Finally, it highlights emerging challenges posed by self-adaptive and synthetic-data-driven LLMs, and proposes future research directions to strengthen model security and reliability.

Detecting Generative-Ai-Enabled Polymorphic Malware: A Semantic-Behavior Approach, Allyson M. Morris

Cybersecurity Undergraduate Research Showcase

AI drastically reduces the effort required to produce malware that mutates both its code and behavior, thereby creating polymorphic and nondeterministic variants in which traditional signatures and many heuristic defenses fail. In this paper, I survey recent developments in AI-assisted malware generation, explain why conventional defenses are insufficient, and propose a layered detection architecture emphasizing semantic behavior, streaming anomaly detection, and defensive generative augmentation. I also outline why this approach generalizes to previously unseen AI-mutated samples, provide an evaluation plan with meaningful metrics, and describe a feasible MVP roadmap for practical deployment. Recent disclosures and threat intelligence further highlight the …

A Comprehensive Evaluation Of Next-Generation Firewall Effectiveness Against Encrypted And Evasive Threats In Enterprise Networks, John Costanzo, Favour Anene

Cybersecurity Undergraduate Research Showcase

Encrypted traffic is becoming a pillar of security and privacy in enterprise networks. According to Google Transparency Report, over 95 percent of internet traffic is encrypted with the use of Hypertext Transfer Protocol secure (HTTPS), Transport Layer Security (TLS) 1.3 and Quick UDP Internet Connections (QUIC). Although encryption safeguards confidentiality and integrity, it has also introduced new blind spots to the conventional security solutions. Encrypted channels are used to hide command-and-control (C2) traffic, issue malware and extract sensitive data without their notice.

To make the issue even harder, the opponents have sophisticated avoidance methods including traffic fragmentation, tunneling, and polymorphic …

Investigating The Security Vulnerabilities Of Ip Cameras: Classifications And Trends From Public Cve Data, Sam Oliver

Cybersecurity Undergraduate Research Showcase

Internet of Things (IoT) devices are increasingly targeted by cyber attacks due to weak authentication, insecure communication protocols, outdated firmware, and many other vulnerabilities. Internet Protocol (IP) cameras, a subset of these IoT devices, are particularly vulnerable and often transmit sensitive information. This paper analyzes vulnerability data from the National Vulnerability Database (NVD) to classify security vulnerabilities affecting IP cameras. Using this dataset, the paper examines the types and frequencies of these vulnerabilities, including authentication bypass, web interface exploits, and default and weak credentials. We additionally examine trends over time and across categories. This research aims to identify the primary …

Sociohydrodynamics: Data-Driven Modeling Of Social Behavior, Daniel S. Seara, Jonathan Colen, Michel Fruchart, Yael Avni, David G. Martin, Vincenzo Vitelli

Data Science Faculty Publications

Living systems display complex behaviors driven by physical forces as well as decision-making. Hydrodynamic theories hold promise for simplified universal descriptions of socially generated collective behaviors. However, the construction of such theories is often divorced from the data they should describe. Here, we develop and apply a data-driven pipeline that links micromotives to macrobehavior by augmenting hydrodynamics with individual preferences that guide motion. We illustrate this pipeline on a case study of residential dynamics in the United States, for which census and sociological data are available. Guided by Census data, sociological surveys, and neural network analysis, we systematically assess standard …

Service With A Smile Or Salesperson Mirroring? Understanding The Flow Of Emotional Contagion In Sales Encounters, Vinh Quoc Trong Luong

Theses and Dissertations in Business Administration

This study examines the directionality of emotional contagion in sales interactions, addressing a critical gap in understanding whether emotions flow primarily from the salesperson to the customer, from the customer to the salesperson, or bidirectionally. While prior research emphasizes customer-driven emotional flow or bidirectional alignment, this study challenges these assumptions by employing categorical Cross-Recurrence Quantification Analysis (CRQA) to assess temporal emotional synchronization in sales dialogues. Leveraging automated sentiment analysis and multi-agent AI evaluation for performance metrics, the research analyzes 166 sales interactions to quantify emotional influence dynamics. Results reveal that salespeople predominantly lead emotional exchanges, exhibiting stronger and more stable …

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Enhancing Data Usability For People With Visual Impairments, Yash Prakash

Computer Science Theses & Dissertations

Human-Data Interaction (HDI) focuses on how individuals engage with, analyze, and extract insights from data. For blind and visually impaired (BVI) users, interacting with data, whether searching for relevant information from structured data (e.g., web data items) or interpreting visualizations to draw insights (e.g., data charts), presents significant challenges. These challenges arise from the complexity and sheer volume of data which cannot be effectively handled by assistive technologies like screen readers and screen magnifiers. Despite its importance, data usability, the ease, efficiency, and satisfaction with which BVI individuals can interact with the data, has received less attention compared to data …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

Enhancing Non-Visual Interaction With Online User-Generated Content, Mohan Krishna Sunkara

Computer Science Theses & Dissertations

The Web has become the dominant medium for our everyday activities, including communication, business, e-commerce, news, and entertainment. Consequently, the online world is experiencing an explosion of User-Generated Content (UGC), particularly on social media platforms and online review systems. To facilitate convenient interaction with UGC, web platforms have adopted various presentation strategies that enable users to efficiently browse and contribute to the UGC. However, these user interfaces are primarily designed for sighted individuals, so they do little to assist blind users who rely predominantly on audio-based screen reader assistive technology. The extant efforts to improve web interaction for blind users …

Unfolding Particle Detector Effects And Solving Qcd Inverse Problem With Generative Ai, Tareq Saeed Alghamdi

Computer Science Theses & Dissertations

Advancements in artificial intelligence (AI) have revolutionized high-energy physics by enabling generative models to address key detector-related Challenges. This work explores the generative model to mitigate smearing, acceptance, and inefficiency in particle detectors, enhancing experimental precision.

We present a generative model-based framework to model and correct detector distortions. Using the Jefferson Lab CLAS g11 experiment as a case study, our approach successfully unfolds detector effects in multi-particle final states while preserving multidimensional correlations despite complex reaction mechanisms. A key focus is addressing the acceptance problem—accurately modeling detector acceptance without computationally expensive simulations. By training generative model-based framework on simulated detector …

Ai-Enhanced Structured Literacy Intervention For Secondary Students: A Case Study Of Science Of Reading, Jennifer Bird

Teaching & Learning Faculty Publications

This study examines the effectiveness of Lexia PowerUp, an AI-powered literacy program, for sixth-grade students requiring Tier 3 reading intervention. Seven sixth-grade students (six boys, one girl; five African American, two Caucasian; all qualifying for free/reduced lunch) participated in a six-month intervention combining 50 minutes of daily small-group instruction with individualized Lexia PowerUp usage. Researchers measured progress through Achieve 3000 Lexile assessments and Lexia PowerUp performance data across three skill strands: Word Study, Grammar, and Comprehension. All participants demonstrated Lexile level improvements from beginning-of-year to mid-year assessments, though students remained below sixth-grade benchmarks (925-1070L). Analysis of Lexia PowerUp progression showed …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Intel Oneapis Esimd, Joseph Wassell

Computer Science Theses & Dissertations

The growing popularity of Computational Fluid Dynamics (CFD) simulations among engineers necessitates the use of GPU acceleration for increased efficiency. NASA FUN3D offers GPU accelerated CFD simulations using unstructured grids across the speed regime from incompressible to hypersonic flows involving reentry. This work focuses on the generalized multi-color point implicit solver used in FUN3D, accounting for roughly half of the run time. Specifically, this work focuses on developing three optimized multi-color linear-solver kernels for the Intel Data Center Max 1550 GPU that is available on the Argonne Leadership Computing Facility’s (ALCF) exascale machine, Aurora. These optimized kernels work for a …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Specialization Or Diversification? Creators’ Strategies On User-Generated Content Platforms, Ziwei Ye

Theses and Dissertations in Business Administration

Recent advancements in digital platforms have reshaped content creation and distribution. User-generated content (UGC), created and shared by internet users, is transforming entertainment, communication, and information sharing. The rise of UGC has fueled the growth of the "creator economy"—an ecosystem of creators, users, and advertisers facilitated by platforms such as YouTube and TikTok. While prior research has primarily explored how UGC platforms incentivize content quantity and quality, this study advances the literature by examining how creators' content strategies influence consumer attention and how platform mechanisms shape this relationship, offering new insights into the interplay between creator behavior and platform design. …

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …