Computer Sciences Commons^™

"Exploring The Training Data Landscape For Ai Based Threathunting For Protecting Intellectual Property", Manzi Siibo, Christopher Kreider

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of the effectiveness that would result in the integration of AI into traditional threat hunting systems. To do so, 10-15 scholarly articles and data sets were evaluated to see the results of AI and machine learning threat hunting versus traditional systems. With so many proven benefits of this integration, this paper also explores how it impacts the protection of Intellectual property which is some of the most important forms of information that threat hunting systems aim to protect.

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Tamos: Task-Aware Multi-Agent Orchestrator System, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Fids: Accelerating Network Intrusion Detection Through Strategic Feature Selection, James Elmore

Cybersecurity Undergraduate Research Showcase

Network intrusion detection systems (IDS) typically analyze complete network flows to identify malicious traffic, requiring flows to conclude before classification. This approach creates detection delays for attacks like Slowloris that intentionally keep connections open for extended periods of time. This paper introduces a novel approach that classifies network traffic using only features available from the first few packets of a flow, enabling faster detection while maintaining high accuracy. We evaluate three random forest models on the CICIDS2017 dataset using expanding sets of features: the first-packet model trained on on features available from the first backward packet, the few-packet model which …

The Evolution Of Russian And Chinese Disinformation Tactics And The Threat They Pose To The U.S. Cybersecurity, Jehovani Sese

Cybersecurity Undergraduate Research Showcase

This paper aims to discuss how disinformation has become the most powerful tool used against the United State by foreign operatives. Of these foreign operatives, Russia and China have shown the ability to use advanced tactics to truly affect the United States security. Often these tools came in the form of state-sponsored media, influence campaigns and fake online identities. This literature review explores the evolution of Russian and Chinese disinformation tactics, examining how these approaches have changed over time and become more sophisticated. This paper will highlight major campaigns which use tools such as bot networks, and social media manipulation. …

Shaped Adversarial Patches And The Ability They Hold, Nathan Hallberg

Cybersecurity Undergraduate Research Showcase

In more recent years the development of computer vision has advanced to be more comprehensive than in the past, with newer applications ranging from autonomous vehicles to security systems. The main application I will be talking about throughout this paper is an object detection algorithm called YOLO (You only look once), this algorithm is particularly significant due to their real-time performance of being able to identify and localize objects within an image in quick timing. However, the strength of these computer vision models is increasingly challenged by adversarial attacks, which manipulate the computer's vision to block a certain part of …

Elder Fraud Metrics And Preventative Measures Of Chesapeake, Virginia, Joey J. Whitmore Jr.

Cybersecurity Undergraduate Research Showcase

Geriatric crime continues to escalate in the digital era, where older individuals are disproportionately being targeted because of their low digital literacy and high susceptibility to online frauds. In this paper, we examine the breadth of elder fraud in Chesapeake, Virginia using FBI Internet Crime Complaint Center (IC3) data and state-level cybersecurity initiatives and survey responses. Older adults aged 60 and up have reported losses of over $3.4 billion in 2023 alone, underscoring the importance of proactive measures. It assesses the public awareness from traditional and AI-based perspectives revealing significant gaps in digital safety literacy and fraud reporting mechanism among …

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

Phishing Attacks And Prevention, Ruth Johnson

Cybersecurity Undergraduate Research Showcase

Phishing attacks have been the number one leading cause of identity theft and financial theft since 1990. The internet is one of the leading places where individuals’ identity is stolen. Many businesses and government agencies have been at risk of cyber phishing attacks from foreign countries. Attacks on Several U.S. federal government agencies have been hit in a global cyberattack by Russian cybercriminals that exploit a vulnerability in widely used software, according to a top us cybersecurity agency (Lyngaas, Government hit cyber-attacks, 2023).

Phishing attacks are cybercrimes where one or many individuals steal sensitive information like passwords, credit card information, …

How Can We Improve Our Security Measures To Safeguard Against Cyber Threats?, Mattea Trotter-Hicks

Cybersecurity Undergraduate Research Showcase

Many people face the issue of having their information stolen without their knowledge of what is happening. I understand that some people don’t pay attention to everything when it comes to making sure their information is being secured properly. There must be set stages for those who don’t know technology as well and will need help with knowing what to do. There are many stories of people going through issues with getting hacked or scammed out of their money or important information. We are going to dive into finding ways to fix the outcome of others knowing what to do …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

48 - Improving Worship Experience: A Secure Application For Generating Worship Questions Using Ai, Hind Aldabagh, Rhys Ferris

Undergraduate Research Symposium

Improving worship Experience: A Secure application for generating worship questions using AI

Rhys Ferris

Under the direction of Hind Aldabagh, School of Cybersecurity

Generating discussion questions for family worship time during the week based on sermon notes presents an innovative and helpful tool to assist pastors in developing worship questions. Instead of using ChatGPT, hosting a small AI model with Mistral provides an alternative solution that better suits our project.

Using prompt engineering, a custom prompt was created within the statement of faith to guide the AI in reading the text file of the sermon notes. The front end consists …

47 - Enhancing Patient Experience: A Secure Self-Service Kiosk For Prescription Status In Pharmacies, Raneem Alarian

Undergraduate Research Symposium

Creating secure and dependable pharmacy kiosk systems is essential for improving both patient satisfaction and the overall efficiency of pharmacy operations. Patients often experience frustration due to long wait times at the pharmacy counter, especially when prescriptions are not ready. As a licensed pharmacy technician, I have witnessed this issue firsthand, which led me to develop a software solution developed to improve pharmacy workflow. This project focuses on the design and execution of a client-server application for a self-service pharmacy kiosk, enabling patients to securely check the status of their prescriptions before they reach the counter. Developed using Visual Studio …

36 - Investigation Of The Digital Footprint Of Scientific Research In Social Media – Preliminary Findings, Lee Logan, Dominik Soos, Sean Baker, Jian Wu

Undergraduate Research Symposium

Title: Investigation of The Digital Footprint of Scientific Research in Social Media – Preliminary Findings

Authors: Lee Logan, Sean Baker, Dominik Soos, Jian Wu

The spread of scientific information and research beyond the confines of academic institutions plays a central role in how the public understands and trusts modern sciences. Social media has become an essential means of dissemination for scholarly news, papers, and other forms of engagement. This research aims to explore how scientific research is disseminated over social media to understand its role as a bridge between peer-reviewed research and the public's overall understanding. To support the research …

32 - Nested Two Level Decomposition For Quantum Computing, Andrew Maciejunes, John Stenger, Dan Gunlycke, Nikos Chrisochoides

Undergraduate Research Symposium

Abstract—We present a two-level decomposition strategy for solving the Vehicle Routing Problem (VRP) using the Quantum Approximate Optimization Algorithm (QAOA). A Problem-Level Decomposition (PLD) partitions a 9-node (72-qubit) VRP into smaller Traveling Salesman Problem (TSP) instances. Each TSP is then further simplified via Circuit-Level Decomposition (CLD), enabling execution on near-term quantum devices. Our approach achieves up to 90% reductions in circuit depth and qubit count. These results demonstrate the feasibility of solving VRPs previously too complex for quantum simulators and provide early evidence of potential quantum utility.

Weapons Of Mass Disruption: How Small States Use Cyber To Resist Larger Powers, Russell Alexander Korb

Graduate Program in International Studies Theses & Dissertations

This paper examines the ways in which small states can engage larger actors using cyber- attacks. Since the end of both World Wars, small states have increased in both numbers and relevance, with strong international institutions and norms against military aggression allowing small states to gain legitimacy by the very act of participating in the international system. However, although small states can now do more than simply choose a larger, stronger benefactor to ward off their enemies, they still cannot defy larger powers outright due to the still- dramatic difference in capabilities between them. Those small states interested in confronting …

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

Stability Analysis In The Twist-Bend Nematic Liquid Crystal Model, Zhenqiang Li

Mathematics & Statistics Theses & Dissertations

The recently discovered twist-bend nematic liquid crystal (LC) phase is characterized by a nanoscale helical modulation of the nematic director n, forming a conical helix along the z-axis at an oblique angle θ. While many models assume a constant cone angle and equal elastic constants K11 = K22 = K33, this dissertation removes both assumptions by considering a fully anisotropic elastic energy with K11 ≠ K22 ≠ K33, and allowing θ to vary spatially. We analyze the stability of this system under frustrated and free boundary conditions using variational methods. …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Integrating Computational Thinking Into K–12 Classroom Instruction, Jorge Valenzuela

STEMPS Theses & Dissertations

In 2016, legislation mandated the integration of computer science (CS) and computational thinking (CT) skills across K–12 instruction in the Commonwealth of Virginia. CT integration is also a focus in many schools throughout the United States. Educators must understand CT/CS core concepts and practices, looking for practical ways to integrate CT across the K–12 curricula. To address this, the current study was conducted. The study compares the effects of the Jigsaw technique and teacher-directed instruction on the participants by surveying their self-efficacy for integrating CT in their teaching. This study uses a pre- and post-test design, and data collection took …

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

A Formal Simulation Model For Discrete Rate Simulation, Thomas J. Tracey

Electrical & Computer Engineering Theses & Dissertations

Simulation is an essential tool for virtualizing systems by creating a representative model of real or hypothetical systems and observing how they change over time. Two predominant simulation paradigms include Discrete Event Simulation (DES) and Continuous Simulation, which both have their strengths and weaknesses. DES does not handle continuous state variables, while continuous simulation handles continuous state variables but encounters errors where these variables have discrete changes in their behavior. This difficulty between the two predominant simulation paradigms prompted the creation of a new simulation paradigm to cover this gap: Discrete Rate Simulation (DRS). DRS as a simulation paradigm focuses …

The Role Of Artificial Intelligence In Workforce Learning And Development: A Systematic Review, Mildred V. Jones

Educational Leadership & Workforce Development Theses & Dissertations

The purpose of this study is to investigate how artificial intelligence (AI) is currently employed in workforce learning and development. The study examined the types of AI employed and the affordances realized for organizations and employees. A PRISMA systematic review methodology was utilized to address the overarching problem statement and answer the three questions guiding the study. The PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta Analysis for Protocols was used to direct each phase of the research. In addition, the Preferred Reporting Items for Systematic Reviews and Meta Analysis was used to conduct the article selection process. …

Leveraging Artificial Intelligence To Strengthen Human Resilience Against Phishing Attacks, Muhammad Mavins

Cybersecurity Undergraduate Research Showcase

Phishing attacks are a major cybersecurity threat, tricking people with fake emails, scam websites, and social engineering tactics. As these attacks become more advanced, traditional security measures are no longer enough to stop them. This paper looks at how Artificial Intelligence (AI) can help detect and prevent phishing while also making people more aware of these threats. Using machine learning (ML), natural language processing (NLP), and behavioral analysis, AI can examine email content, sender behavior, and metadata to spot phishing attempts. AI-powered cybersecurity training can also teach people to recognize and respond to phishing by using personalized phishing tests and …

Rapid Prediction Of Coastal Flooding With Deep Neural Networks, Ali Shahabi, Navid Tahvildari

Graduate Student Government Association Research Conference

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from extreme storm tides and recurrent nuisance flooding. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, enormous efforts have been made to utilize machine learning (ML) based data-driven models for the emulation and prediction of storm tides. However, flood advisory systems still rely on running computationally demanding real-time hydrodynamic models. because developing highly reliable ML-based models suitable for real-time forecasting and capable of capturing any surge levels is challenging. …

Zombie-Auras: Genai And Hybrid Text Production, Joshua Nieubuurt

English Faculty Publications

This paper explores the iterative evolutions of textual production and their impact on the “aura” of texts, as conceptualized by Walter Benjamin. The study identifies three key phases of textual production: the natural, the mechanized, and the digitized, each progressively displacing the “cult value” of texts. This cult value is lost through increased ease of creation, reproduction, dissemination, and dislocation of creators and audiences in time and space. The advent of Generative AI (GenAI) marks the latest evolution, transforming the “aura” into a memetic “zombie” form—familiar yet opaque, evoking both the sublime and fear. By examining Benjamin’s notion of “aura” …

Bridging The Gap: Understanding And Mitigating Csrf Threats In Service Worker Environments, Sivakanesan Dhanushkanda, Mustafa A. Ibrahim

Graduate Student Government Association Research Conference

Progressive Web Applications (PWAs) are gaining popularity due to their rich features. Service Workers (SWs), one of its integral components, make this possible by providing users with offline functionality, improved performance, and effective caching techniques. SWs act as proxies positioned between the client browser and the web server, capable of intercepting requests and responses. Recent research has revealed that, despite being designed with security in mind, there are ways to circumvent these security precautions and launch various attacks.

Sensitive functions in JavaScript are functions that can introduce security vulnerabilities if not properly coded or validated. These functions can manipulate the …