Digital Commons Network^™

Phishing Restraint: University Simulated Phishing Campaigns, Alexander M. Abou Khir

Cybersecurity Undergraduate Research Showcase

Universities face heightened vulnerability to phishing attacks due to their open information-sharing culture and diverse user populations. This study examines how phishing exploits human factors within campus environments and evaluates three major training strategies: embedded phishing, microlearning, and role-based instruction to understand their individual and combined effectiveness. I explored studies that implement these strategies in pairs and use the strategies alone, identified trends in susceptibility reduction, behavioral reinforcement, and contextual relevance. I suggest that, while each method independently improves user awareness, multiple approaches offer stronger, more adaptable protection by addressing both psychological triggers and role-specific risks. The paper contributes a …

The Texture Of A Threat: Adversarial Training, Cnns, And Obfuscated Malware Detection, Kaelyn Haynie

Senior Honors Theses

Accurately detecting malicious programs is an expanding field of research for machine learning (ML), with a novel approach incorporating a bytecode-to-image pipeline that produces images representative of software. These images are provided to convolutional neural networks (CNNs) to be examined for malicious pattern indicators. However, CNNs struggle to generalize these patterns effectively while still being robust against adversarial data, an issue which this research addresses with adversarial training. In this paper, three unique CNN architectures (a DBFS-MC-inspired baseline, MIRACLE, and PSP-CNN) are trained for binary classification with 15,000 benign and malicious software samples encoded into images for Android, Windows, and …

Leveraging Convolutional Neural Networks For Through-The-Wall Radar Imaging: Challenges, Impacts, And Future Directions, Tumaini Edgar, Abdulla F. Ally, Abdi T. Abdalla

Tanzania Journal of Engineering and Technology (TJET)

Through-the-wall radar imaging (TWRI) is an essential technology for military and rescue applications; however, its performance in detecting and visualizing high-quality images of targets behind walls is significantly degraded by multipath reflections and signal attenuation. This paper reviews the current state of TWRI and its challenges, and explores the transformative potential of deep learning, particularly convolutional neural networks (CNNs), in addressing these challenges. Peer-reviewed articles published from 2018 to 2024 were analysed to examine CNN applications in addressing TWRI challenges. The analysis reveals that using CNNs, TWRI systems can be more effective by filtering wall distortions, reducing noise, lowering computational …

Hijacking The Prompt: A Survey Of Prompt Injection Attacks, Detection, And Defense In Large Language Models, Edward J. Griggs

Cybersecurity Undergraduate Research Showcase

Prompt injection attacks, ranked the number-one vulnerability in AI systems by OWASP's 2025 Top 10 for Large Language Model Applications, remain largely unsolved, and this survey examines why. As large language models (LLMs) are deployed across enterprise workflows, agentic systems, and consumer tools, their fundamental inability to distinguish trusted instructions from untrusted user data has created a persistent and expanding attack surface. This paper presents a structured taxonomy of prompt injection attack vectors, including direct injection, indirect injection, multimodal attacks, tool and agent exploitation, hybrid chained techniques, and autonomous propagating threats. These vectors are mapped across five impact categories (data …

Umm (Ultimate Memory Manager): A Note–Taking App Built From Scratch, Caleb M. Early

ASPIRE 2026

For my honors project, I’m building UMM, an advanced note-taking application built entirely from scratch. I’m someone who takes many notes and primarily uses OneNote and Notion, but over time have found myself wanting something faster and more flexible than any note-taking application I could find. UMM is my attempt to create the note-taking app I wish I had, as well as learn how to create such a program.

Instead of relying on pre-made code, I built my own systems for how documents are structured, edited, saved, as well as how the cursor moves, text is formatted, and how selections …

Cybersecurity Integration In Research Stage Connected Devices Within Healthcare Environments, Klemens Koszarek

Master's Theses (2009 -)

The rapid growth of connected devices in healthcare environments has significantly expanded the cybersecurity threat landscape, extending the potential risks beyond traditional hospital networks. Regulatory guidance from the FDA and risk management frameworks such as the NIST Cybersecurity Framework (NIST CSF) provide an approach for managing cybersecurity risk in connected systems. However, these principles are not always applied during early stages of device development. As a result, security consideration may be deferred until later stages of development, allowing for vulnerabilities to persist in systems as they move toward clinical or commercial deployment. This study presents a structured method for integrating …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Thesis/ Dissertation Defenses

Air pollution is one of the most important and devastating environmental issues that heavily affects public health around the world and it’s the cause of approximately 4.2 million early deaths. This thesis focuses on further improving forecasting models by introducing a zonal approach and satellite-based spatial validation. The main objective is to explore a zonal approach with the ground station data and to add a spatial component using satellite imagery to improve the accuracy of the results. It follows a four-stage evolution framework while focusing on the three different zones chosen. The four stages introduced different aspects which include a …

Limitations Of Signature-Based Network Intrusion Detection Under Modern Traffic Conditions, Henry Guidry

Cybersecurity Undergraduate Research Showcase

Network Intrusion Detection Systems are tools used to monitor network traffic and alert to suspicious or harmful activity before it can cause harm. Signature-based versions of these systems are a foundation for intrusion detection, operating by finding common patterns and forming malicious signatures. However, three developments in modern network environments have greatly impacted the significance of Network Intrusion Detection Systems. These three developments are the near-complete adoption of end-to-end encryption, the use of sophisticated packet fragmentation techniques, and the processing demands of high-throughput networks. Encryption makes deep packet inspection practically infeasible by transforming inspectable payloads into ciphertext, forcing NIDS to …

The Efficacy Of Manganese Sulfate As A Negative Contrast Agent For Magnetic Resonance Cholangiopancreatography, Zainab Abdulla Mankhi

Karbala International Journal of Modern Science

The current study aims to find an alternate oral contrast media for use in magnetic resonance cholangiopancreatography (MRCP) that satisfies the following criteria, the greatest imaging quality safety, no or few side effects, and low cost. The present study created an oral contrast agent sample (solution) by dissolving a one tablet of manganese sulfate supplement (taken daily dose) in 200 ml of distilled water. Thirty-three volunteers assessed the sample using MRCP examine pre and post contrast. By evaluating the signal intensity to compute contrast (C), signal to noise ratios (SNR), and contrast to noise ratio (CNR), the resulting MR images …

Negotiating Digital Identities With Ai Companions: Motivations, Strategies, And Emotional Outcomes, Renkai Ma, Shuo Niu, Lingyao Li, Alexander Hirth, Ava Brehm, Rowajana Behterin Barbie

Computer Science

AI companions enable deep emotional relationships by engaging a user's sense of identity, but they also pose risks like unhealthy emotional dependence. Mitigating these risks requires first understanding the underlying process of identity construction and negotiation with AI companions. Focusing on Character.AI (C.AI), a popular AI companion, we conducted an LLM-assisted thematic analysis of 22,374 online discussions on its subreddit. Using Identity Negotiation Theory as an analytical lens, we identified a three-stage process: 1) five user motivations; 2) an identity negotiation process involving three communication expectations and four identity co-construction strategies; and 3) three emotional outcomes. Our findings surface the …

Creating Disability Story Videos With Generative Ai: Motivation, Expression, And Sharing, Shuo Niu, Dylan Clements, Hyungsin Kim

Computer Science

Generative AI (GenAI) is both promising and challenging in supporting people with disabilities (PwDs) in creating stories about disability. GenAI can reduce barriers to media production and inspire the creativity of PwDs, but it may also introduce biases and imperfections that hinder its adoption for personal expression. In this research, we examine how nine PwD from a disability advocacy group used GenAI to create videos sharing their disability experiences. Grounded in digital storytelling theory, we explore the motivations, expression, and sharing of PwD-created GenAI story videos. We conclude with a framework of momentous depiction, which highlights four core affordances of …

When Generative Ai Is Intimate, Sexy, And Violent: Examining Not-Safe-For-Work (Nsfw) Chatbots On Flowgpt, Xian Li, Yuanning Han, Di Liu, Pengcheng An, Shuo Niu

Computer Science

Content Warning: This paper contains sexually explicit and violent images and text. User-created chatbots powered by generative AI offer new ways to share and interact with Not-Safe-For-Work (NSFW) content. However, little is known about the characteristics of these GenAI-based chatbots and their user interactions. Drawing on the functional theory of NSFW on social media, this study analyzes 376 NSFW chatbots and 307 public conversation sessions on FlowGPT. Findings identify four chatbot types: roleplay characters, story generators, image generators, and do-anything-now bots. AI Characters portraying fantasy personas and enabling hangout-style interactions are most common, often using explicit avatar images to invite …

The Promises And Perils Of Using Llms For Effective Public Services, Erina Seh-Young Moon, Matthew Tamura, Angelina Zhai, Nuzaira Habib, Behnaz Shirazi, Altaf Kassam, Devansh Saxena, Shion Guha

Health Services and Informatics Research

Governments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family’s engagement with the system. With growing optimism around AI, governments are pushing for its integration but concerns regarding feasibility and harms remain. Through collaborations with a large Canadian CW agency, we examined how LocalLLM and BERTopic models can track CW case progress. We demonstrate how the tools can potentially assist workers in opportunistically addressing gaps in their work by signaling case progress/deviations. And yet, we also …

From Oversight To Insight: Transforming Cybersecurity Governance In Boardrooms, Tooba Aamir, Georgia Psaroulis, Marthie Grobler, Helge Janicke

Research outputs 2022 to 2026

Cybersecurity governance is increasingly critical in a digital economy, with board directors playing a central role in shaping organisational resilience. Directors are pivotal in setting cybersecurity strategies and carrying fiduciary obligations that extend to digital risk oversight. This study examines the cybersecurity literacy and governance practices of Australian board directors through a qualitative interview study with 13 participants. Findings reveal a substantial gap in directors' knowledge and confidence, undermining effective oversight and informed decision-making. This deficit limits their ability to interrogate risk reports, challenge assumptions, and steer investment in line with organisational resilience goals. In response, we propose a Board …

Ai’S Role In Searching For Evidence: Friend And Foe, Barbara (Basia) Delawska-Elliott, Brandon Wilkinson

Publications 2026-present

No abstract provided.

Characterizing User-Reported Risks Across Llm Chatbots, Lingyao Li, Renkai Ma, Zhaoqian Xue, Junjie Xiong

Computer Science Faculty Research & Creative Works

As Large Language Models (LLMs) become increasingly integral to daily life, users are engaging with multiple LLM chatbots for various needs; however, prior research on LLM risks often remains lab-based or focuses on single LLMs like ChatGPT or singular risks like privacy. To gain a multi-risk, cross-chatbot understanding of user experiences, we analyze Reddit discussions around seven major LLM chatbots using the NIST AI Risk Management Framework. We find that user-reported risks are unevenly distributed and chatbot-specific: ChatGPT is associated with safety and fairness concerns, Gemini with privacy, and Claude with security and resilience. Less frequent risks, such as explainability …

Dual History Enhancement With Hybrid Hypergraph-Graph Networks For Temporal Knowledge Graph Reasoning, Kailun Ye, Xiangjie Kong, Yuchao Zhang, Xuan Wang, Linan Zhu, Jiaxin Du, Guojiang Shen, Jianxin Li

Research outputs 2022 to 2026

Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual …

Does Culture Translate? A Case Study On Artificial Intelligencegenerated Text Concerning Japanese-Style Employment, Kanji Kitamura

Languages, Literatures, and Cultures Faculty Publications

Translation matters to international business when it has a discernible impact on firm performance or critical business processes. Current developments include machine translation, which is evolving in tandem with generative artificial intelligence (AI). On one hand, useful translation apps have become available to help the public communicate internationally. On the other hand, recent research concludes that even machine translation's best output still requires post-editing by a human. This argument implies that a machine-translated, grammatically accurate text may not always make sense to a human audience, possibly because of a semantic-pragmatic issue beyond the lexical level. This paper regards it as …

Artificial Intelligence Moderation In Online Gaming: A Cybersecurity Analysis Of Risks And Defenses, Labib Khan

Cybersecurity Undergraduate Research Showcase

This research paper will examine the role of artificial intelligence (AI) moderation systems in enhancing cybersecurity within online gaming environments. As multiplayer platforms increasingly rely on real-time text, voice communication, and user-generated content, developers have implemented AI-driven tools such as natural language processing (NLP), speech recognition, and behavioral analytics to detect harassment, toxic behavior, cheating coordination, and other malicious activity. These systems enable gaming companies to efficiently monitor large volumes of player interactions, improving response times and helping maintain safer and more controlled digital environments for users across global gaming communities of varying sizes and activity levels.

While these technologies …

Older Adults And Emerging Technology Fraud In The Ai Deepfake Era, Thiago Neves

Research Days

Artificial intelligence has accelerated faster than society's ability to adapt, leaving older adults extremely vulnerable to AI-generated fraud. Americans over age 60 lost $4.9 billion to scams in 2024, 43% more than the previous year. In this research, I investigate how digital illiteracy, combined with AI-generated deepfakes, creates this crisis. Older adults struggle with three principal vulnerabilities: distinguishing legitimate sites from scams, judging whether online information is truthful, and understanding how algorithms use their data. AI weaponizes these gaps through voice clones, synthetic video calls, and personalized phishing emails that avoid the trust cues seniors tend to rely on. I …

Ai's Double Edged Sword: Fighting Against Synthetic Csam, Shekhinah Adra Green

Cybersecurity Undergraduate Research Showcase

The rapid advancements in generative artificial intelligence has introduced new challenges in the production and distribution of synthetic child sexual abuse material (CSAM). AI has the capabilities of creating highly realistic imagery and videos, which  raises serious legal and ethical concerns, increasing the risk of harm, exploitation, and revictimization.

This paper discusses the legal improvements needed in order to lower the change of legal loopholes, how digital forensic analyst use advanced tools to identify and investigate synthetic material, and different methods to start the reduction of synthetic CSAM.

Library Discovery Kiosks Using Microsoft Webview2, Andres Cazares Reyes, Tom Tran

Library Services Publications

This presentation describes how our library developed a Primo discovery search kiosk using Microsoft WebView2. The session will cover kiosk inactivity reset automation, navigation controls, and deployment.

Advice For Incorporating Ai Tools Into Your Legal Practice, Celia Bigoness, Robert A. Mackenzie, David J. Reiss

Cornell Law Faculty Publications

We have been speaking with many lawyers and law students about using generative artificial intelligence (AI) tools in their legal practice. We are struck by the fact that many of them have not been experimenting much, if at all, with the tools that are available to them - although many acknowledge that their clients are increasingly integrating generative AI into their businesses. We have been integrating a lot of these tools into our own professional lives, and here are some tips to help lawyers and law students get comfortable with AI tools that can help them, in big ways and …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Bio-Cybersecurity: Securing The Healthcare Industry, Amanda D. Coleman

Cybersecurity Undergraduate Research Showcase

Bio-cybersecurity refers to the aspect of cybersecurity that applies to the biological sciences and the protection of digital biomedical information. Today’s healthcare industry has evolved with the enhancement of internet and biomedical technology. While hospitals and private medical providers remain compliant with the Health Information Portability and Accountability Act (HIPAA) through traditional means of securing documented patient information, the emergence of beneficial internet-based healthcare services like virtual appointments and digital patient records requires new policies and healthcare cybersecurity frameworks to protect sensitive information from unauthorized access. This paper examines the role of cybersecurity in healthcare, the vulnerabilities that exist and …

A Comprehensive Survey Of Agentic Ai: Design Principles, Security Risks, And Ethical Consideration, Md Shaba Sayeed

ATU Scholars Symposium

In the past several years, the world has managed to transition away from simple automation to independent AI systems. Agentic AI is an agent that can work independently, carrying out all essential plans and implementations without any kind of supervision from a human being. This review has tried to demonstrate the transformative impact that Agentic AI brings to contemporary models of intelligence by means of synthesis of perception, reasoning, and goal. We utilized the phrases Agentic AI, autonomous AI, multi agent systems as keywords in Google Scholar, ScienceDirect, arXiv, and other digital libraries. We have used these 38 main papers …

Proactive Mental Health Assistance Via Agentic Llm Chatbots With Retrieval-Augmented Generation, Shaira Wajiha

ATU Scholars Symposium

Mental health challenges such as anxiety and depression are widespread, yet often remain unaddressed due to stigma, financial barriers, limited access to professionals, or personal reluctance. To address this gap, we present an accessible AI-powered chatbot that provides preliminary conversational support with empathetic, context-aware responses. The chatbot is trained and evaluated on two publicly available counseling conversation datasets from Huggingface, enabling it to learn and maintain therapeutic dialogue patterns. We compare three advanced LLMs, such as Llama3.1, Mistral 7b, and Qwen3, evaluating them on relevance, empathy, conciseness, and contextual understanding, and all models demonstrate high response quality. Incorporation of a …

Automatic Labeling Of Real-World Pmu Data: A Weakly Supervised Learning Approach, Yunchuan Liu

Research Days

This paper presents a weakly supervised learning framework for real-world event identification in transmission networks using phasor measurement unit (PMU) data. The growing integration of renewable energy sources has introduced greater variability in grid conditions, intensifying the need for accurate event detection. Although high-resolution PMU measurements enable event identification to be formulated as a classification problem, traditional supervised learning approaches are hindered by the scarcity of labeled data, and acquiring large-scale, high-quality labeled PMU datasets remains prohibitively expensive. To overcome this challenge, we propose an automated PMU data-labeling method that combines domain knowledge with machine learning techniques through the use …

Blazewatch: A Web-Based Management System For Volunteer Fire Departments, Michael J. Heinzen, Locke G. Weisler, Hunter S. King, Ryan N. Williams

ATU Scholars Symposium

BlazeWatch is a web application developed for rural volunteer fire departments. Volunteer fire departments often rely on manual processes and paper records systems to manage district fire dues, emergency call documentation, and equipment tracking. These inefficiencies can increase administrative workload and reduce efficiency. BlazeWatch is a secure full-stack web application designed to streamline administrative management for volunteer fire stations while providing a public-facing interface for general department information as well as a way for users to pay their fire dues. The system is built using an Angular frontend and a .NET backend with Identity-based authentication to ensure secure access control. …

Moneyup: A Predictive Financial Management System For College Students, Isaiah J. Adams, Malaya E. Wilburd, Dan V. Le, Joshua P. Golden

ATU Scholars Symposium

College students often lack accessible tools that combine real-time financial tracking, mobile accessibility, predictive analytics, and secure system design, leaving many without structured insight into their spending behavior. MoneyUP is a full-stack financial management platform developed to address these challenges through a secure, data-driven budgeting system deployed as both a web application and a cross-platform Flutter mobile application. The system integrates the Plaid API in its Sandbox environment to synchronize simulated banking data for secure testing without exposing live financial credentials. Transaction data is processed and stored using Supabase with a relational PostgreSQL database structured to enforce normalization, referential integrity, …