Cybersecurity Commons^™

Video Game Hacking, A General Problem With Generalized Solutions, Luke Rowe

Master's Theses

As video games continue to get more popular and lucrative, the number of malicious actors seeking to exploit them grows with it. As this industry expands, so does the importance of securing games against cheating and abuse. This thesis aims to educate developers to help mitigate the abuse of video games by these malicious actors. The goal of this thesis is to provide a foundational framework for thinking like a hacker and how to make games harder to abuse once a hacker bypasses conventional anti-cheat software.

This thesis outlines some of the most common cheating methods and provides general context …

Spos: An Attestation Solution For The Detection And Mitigation Of Point Of Sale Malware, Damian Singh Dhesi

Master's Theses

Securing 95% of card present transactions, accounting for billions of transactions a year, has made EMV the premier protocol for card-based payment. Created by and named after Europay, Mastercard, and Visa, the EMV protocol provides multiple solutions to resolve security concerns with the outdated, swipe-based, magnetic stripe payment. Such solutions are Chip and PIN which provides a more secure transaction at a significant time cost and EMV contactless which provides improved security to Chip and PIN at greater ease of use with its quick, tap-to-pay based payment. However, regardless of how secure the EMV protocol makes the card side of …

Efficient Gan-Based Adversarial Example Generation Against Ml-Based Network Intrusion Detection Systems, Darren D. Hartono

Master's Theses

In the realm of network security, Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating malicious activities targeting networked devices. Traditionally, these systems have relied on signature-based and anomaly-based detection techniques. However, the increasing complexity and adapt- ability of cyber threats have driven the adoption of Machine Learning (ML) ap- proaches in modern NIDS, significantly improving their ability to detect a wider range of attack vectors. Despite these advancements, ML-based NIDS remain vulnerable to adversarial examples—deliberately crafted inputs designed to mislead models and trigger incorrect classifications. Originally identified in the field of computer vision, adversarial examples now pose …

Exploiting Compiler-Introduced Vulnerabilities In C: A Cross-Compiler And Cross-Architecture Analysis Of Undefined Behavior, Erik Mccutchen

Master's Theses

Compilers are a critical component in generating secure software across engineering disciplines. However, languages like C that permit undefined behavior introduce a fundamental tension between the compiler’s interpretation of undefined behavior and the security of the generated code. This tension can result in security vulnerabilities that, from the programmer's perspective, are ``created'' by the compiler. The widespread use of these languages, combined with the complexity of modern optimizations and limited developer visibility into compiler behavior, makes these vulnerabilities both pervasive and difficult to detect.

Building on prior work, this thesis refines a dataset of C code snippets that exhibit Compiler-Introduced …

Property Testing Ai: An Efficient Frontier, Paul Sopher Lintilhac

Dartmouth College Ph.D Dissertations

In this dissertation, we take a step towards addressing the major problem of a lack of standardized and rigorous approaches to testing and evaluation of AI systems. Taking inspiration from both the fields of Property Testing and Property Based Testing (for programs), we develop a novel taxonomy of partially overlapping classes of properties of AI systems, including simple properties, compound properties, higher order properties, data relation properties, and architecture-utility properties. We argue that this taxonomy categorizes a diverse set of AI traits -- including accuracy, fairness, robustness, monotonicity, point-wise and global privacy properties, sensitivity, and more -- according to the …

Some Studies On Information Set Decoding Algorithms And Universal Hash Functions, Sreyosi Bhattacharyya

Doctoral Theses

This thesis presents some studies on Information Set Decoding algorithms and Universal Hash Functions. In the context of Information Set Decoding (ISD) the thesis studies time/memory trade-off of ISD algorithms and in the context of universal hash functions, the thesis studies design and efficient implementations of polynomial hash functions defined over prime order fields. A cornerstone of ISD algorithms is the algorithm proposed by Stern and it introduced the meet-in-the-middle collision search approach to ISD algorithms. Though this algorithm is more efficient in terms of asymptotic time complex- ity than the preceding algorithms proposed by Prange, Lee and Brickell and …

Dsa-Api: Data Standardization Automation Using Ai-Powered Apis, Andrew Asher Turner

Master's Theses

The common factor with current implementations of Artificial Intelligence (AI) is data. Companies are constantly looking for new ways to analyze data, but it comes in various formats: Text, Comma Separated Value (CVE), JavaScript Object Notation (JSON), Extensible Markup Language (XML), and Excel. How can AI be adapted to standardize formats for data analysis, integration, and digestion efficiently? Published research acknowledged that Machine Learning (ML) and AI can provide an automated method to speed up this process and limit the human decision-making error. With the advancement in AI, Application Programming Interfaces (APIs) prompt the idea that they can take in …

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

Phishy Pages - The Design Of A User-Interactive Website For Phishing Attack Evaluation, Evan C. Gregory

Honors Theses

Phishing attacks are a widespread, malicious phenomenon. These attacks steal people’s personal information, causing them ruin and lining the pockets of criminals. What makes them so dangerous is that they come in a variety of forms, including emails, websites, phone calls, and social media can be vectors for attackers. Fortunately, these attacks can be stopped by informing potential victims of common signs to look out for. Training is one of the best methods people use to teach web-users how to protect themselves. To train them, however, users must be taken through many examples of phishing attacks to learn the characteristics …

Harnessing Neurodiversity And Artificial Intelligence In Education To Bridge The Cybersecurity Workforce Gap, George Antoniou

Faculty and Staff Publications & Presentations

This perspective paper examines how neurodiversity and artificial intelligence (AI) can jointly address the critical workforce shortage in cybersecurity. Drawing on peer-reviewed research, industry reports, and case studies, it explores how neurodivergent individuals—such as those with autism spectrum disorders, ADHD, and dyslexia—possess strengths in pattern recognition, logical reasoning, and attention to detail that align with cybersecurity demands. AI-based educational tools, including adaptive tutoring systems, scenario-based simulations, and real-time analytics, can personalize learning for neurodiverse students, enhancing engagement and skill mastery. The paper discusses how these targeted interventions not only accelerate knowledge retention and practical competence but also foster greater inclusion …

Leveraging P4 Programmable Switches For Resilient Operation And Design Of Phasor Measurement Unit Networks, Eva Casto

Electrical Engineering and Computer Science Undergraduate Honors Theses

The power grid utilizes a device called the phasor measurement unit (PMU), allowing power system administrators to remotely monitor and manage the state of the grid in Wide Area Monitoring Systems (WAMS). The advantages of PMUs – such as fine-grained, time-synchronized measurements and efficient, decentralized monitoring – are what make them key devices in the power grid. However, PMU technology also comes with new threats of the digital age, like malfunctions and cyberattacks, which can result in missing and faulty measurements that compromise power grid observability. P4 programmable networks can be used to detect faulty PMU data in a decentralized, …

Understanding The Impact Of Ransomware On Biotechnology, Tswvyim Vang

Electronic Theses, Projects, and Dissertations

Biotechnology encompasses the use of research on both biology and technology to create products that can advance in areas such as healthcare and agriculture. Because of the technology and products that arise from the use of biotechnology, the industry is especially targeted by cyber-attacks. A prominent type of cyber-attack that is utilized by cyber criminals on biotechnology is ransomware. The goal of this project is to determine the impact of ransomware on biotechnology. The research questions posed are: Q1) What are real examples of ransomware attacks that have occurred on biotechnology companies and what patterns can be identified by these …

Survey On Application Of Large Language Models In Network Attack And Defense, Prisha Purohit

Honors Theses

The emergence of Large Language Models (LLMs) has significantly transformed the technological and cybersecurity landscape, introducing both unprecedented opportunities and formidable challenges. With the public release of ChatGPT in 2022, LLMs have gained global prominence, redefining natural language processing capabilities and enabling advancements across various fields. In cybersecurity, these models represent a dual-use technology: while they offer powerful tools for threat detection, automated analysis, and security training, they also pose risks when leveraged by malicious actors for phishing, social engineering, and the creation of evasive malware. This thesis presents a comprehensive literature review exploring the dual roles of LLMs in …

Capturing The Digital Scene: Applying Routine Activity Theory To Iot Smart Frames, Jordan Bakar

Theses/Capstones/Creative Projects

This project investigates the forensic risks and investigative challenges posed by smart frames, which are WiFi-enabled Internet of Things (IoT) devices used to store, display, and share digital media. These devices often collect and synchronize sensitive media, metadata, and behavioral logs across cloud ecosystems that lack adequate transparency and privacy safeguards. Routine Activity Theory (RAT) provides a criminological framework for examining how the convergence of a motivated offender, a suitable target, and the absence of capable guardianship creates opportunities for misuse and forensic exploitation. Smart frames represent ideal targets because of weak default security configurations, passive data synchronization, and limited …

A Comprehensive Performance Comparison Of Machine Learning And Federated Learning For Intrusion Detection In Vehicular Ad-Hoc Networks Using Can-Bus Data, Tim Leonhardt

Honors Theses

Federated Learning (FL) is a Machine Learning (ML) approach that decentralizes training across distributed devices, eliminating the need to centralize data. Unlike traditional ML, where models are trained on aggregated data, FL sends a global model to multiple nodes for local training, with updated parameters transmitted back to the server for aggregation. This process preserves data privacy, making FL ideal for sensitive applications like cybersecurity. However, FL introduces challenges such as data heterogeneity, communication overhead, and difficulties in achieving model convergence, which can impact performance.

This study investigates a fundamental assumption in ML and FL research: that the superior performance …

5g Network Slice Vulnerabilities And Exploit Chaining Through Enablers, John Breeden

Electronic Theses and Dissertations

Network slicing provides fundamental support for the enhanced features of 5G. Network slicing enablers, such as software-defined networking and network function virtualization facilitate the separation of the physical distributed infrastructures and the functions that create isolated slices. With this framework, the network slice is no longer under the control of a single entity. Multiple infrastructure providers share responsibility for the slice. The 5G architecture derives from multiple services, distributed over great distances, and managed by multiple parties. Each enabler and provider adds vulnerability to network slicing. We examine the interweaving of these enablers to identify vulnerabilities, discuss potential mitigation, and …

Cybersecurity's Pr Problem: The Education Gap Fueling Mfa Aversion, Tyler M. Stafford, Catherine Dwyer

Honors College Theses

Through surveying individuals with no professional experience in cybersecurity, this study examines the relationship between awareness and education surrounding security controls and end users’ willingness to adopt them. The findings reveal a strong link between understanding the effectiveness of these controls and user comfort, indicating that as end users’ understanding increases, so does their willingness to use the controls. Working in both identity and access management (IAM) and human risk management, I observed what appeared to be a connection between security education and positive attitudes toward security more broadly, but found limited research statistically linking the two. This study’s findings …

Key-Based Authentication Scheme For Evtol Drones Using Chebyshev Chaotic Maps, Eduardo A. Hernandez Escobar

Master's Theses

The development of electric Vertical Take-Off and Landing (eVTOL) drones signifies a substantial advancement in urban air mobility, ready to transform transportation models in densely populated regions. These advanced drones, distinguished by their capacity to function in limited spaces and their minimized environmental impact, are set to transform individual, shipping, emergency services, and public safety activities. Nonetheless, like any transformational technology, the implementation of eVTOL systems presents many challenges, especially in the realm of cybersecurity. Adding many devices and entities to an eVTOL network increases the risk of privacy and security attacks. This paper proposes a key-based authentication scheme that …

Securing Distributed Energy Resources: A Secure Gateway For Modbus To Solid Communication Using A Raspberry Pi, Donna R. Thakadipuram

Electrical Engineering and Computer Science Undergraduate Honors Theses

As distributed energy resources (DERs) such as solar panels, wind turbines, and battery storage systems become more common, securing their communications has become increasingly important. Many of these systems still rely on legacy communication protocols such as Modbus, which were not designed with cybersecurity in mind. This project addresses this challenge by developing a secure communication gateway that allows Modbus RTU devices to interface with decentralized Solid pods, which are personal data storage units that give users control over their information. This system is built on a Raspberry Pi 4, and it translates telemetry data from Modbus into a Solid-compatible …

Managing Software Dependency Risks In Web Applications, Christopher Alan Scott

Electronic Theses and Dissertations

Web applications commonly rely on third-party software dependencies to reduce development time. This thesis examines how vulnerabilities in a dependency chain propagate to compromise an application. It analyzes two vulnerable Markdown libraries from the npm and Composer dependency ecosystems, both of which are used for managing packages in applications developed with JavaScript and PHP. The analysis demonstrates how each library’s sanitizing functions—intended for removing unsafe user input when transforming Markdown text to HTML—are defeated to achieve a cross-site scripting exploit and take control of the application. The paper discusses potential business impacts of a compromise, underscoring the need for security …

Network-Based Attacks In Cloud Computing In 2020-2024, Yaswanth Sai Manikanta Anguluri

Electronic Theses, Projects, and Dissertations

As the use of cloud technologies has increased in the past five years, the number of network attacks is also increasing. During 2020 to 2024, there are lot of changes in cloud technologies which led to various network attacks in the cloud computing environments from 2020 to 2024. This study investigates the evolution of network-based attacks in cloud environments from 2020 to 2024. Data was collected from Kaggle website to analyze the trends of the evolution of network-based attacks. The research questions are: (Q1) How do the trends change in network-based attack from 2020 to 2024 and why? (Q2) Which …

Behind The Screen: Understanding The Human Firewall In Cybersecurity, Arun Venkitanarayanan

Emergency Preparedness, Homeland Security, and Cybersecurity

No abstract provided.

Deepfakes On Trial: Developing A High-Accuracy, Court-Admissible Ai Pipeline For Deepfake Detection In Corporate Fraud Litigation, Aiden J. Green

Honors College Theses

As deepfake technology advances, cybercriminals are increasingly using AI-generated videos and audios to impersonate executives and carry out sophisticated CEO fraud schemes. These synthetic forgeries target human trust and corporate communication systems, creating an urgent need for forensic tools capable of authenticating digital evidence with legal accuracy. This thesis presents a forensic-grade AI deepfake detection pipeline designed for this purpose, emphasizing courtroom admissibility, reproducibility, and evidentiary integrity. Built entirely with free, opensource tools, the framework combines metadata analysis, AI-powered spectrogram analysis, neural artifact detection, and facial manipulation recognition into a transparent workflow that accurately identifies synthetic media. It was trained …

Does The Transit Industry Understand The Risks Of Cybersecurity And Are The Risks Being Appropriately Prioritized?, Scott F. Belcher, Terri Belcher, James Grimes, Lusa Holmstrom, Andy Souders

Mineta Transportation Institute

The intent of this study is to assess the readiness, resourcing, and capabilities of public transit agencies to detect, identify, be protected from, respond to, and recover from cybersecurity vulnerabilities and threats. This study is an update of the 2020 Mineta Transportation Institute (MTI) study, “Is the Transit Industry Prepared for the Cyber Revolution? Policy Recommendations to Enhance Surface Transit Cyber Preparedness.” In the previous study, the authors found that the transit industry was ill-prepared for cybersecurity attacks. Unfortunately, after four years and the development of new, and often free, resources, the situation has not markedly improved. In fact, this …

An Analysis Of Face Morphing Presentation Attacks Against Facial Recognition Systems, Joshua Breininger

Theses and Dissertations

Security has been a problem for human society for as long as history has been recorded. The identification of people is an ongoing, ancient battle, with a variety of methods that only become more complex with time. The Romans performed censuses, ciphers have been used for thousands of years in the pursuit of security, and in modern day we own identifications and governments keep track of who lives in their country with citizenship and licenses. The question of ”Who are you?” is vital for society to function, which opens up a massive field of potential for how to ask that …

Adversarial Machine Learning: Methods For Attacks And Defenses, Minh Hao Van

Graduate Theses and Dissertations

With the rapid development of machine learning in real-world applications, enhancing security plays an important role. Adversarial machine learning focuses on understanding malicious actions from attackers and developing defensive techniques against such threats when deploying machine learning systems. An attack can occur in different scenarios, such as poisoning attacks during the training stage and evasion attacks during the testing stage. Although extensive research has explored defense strategies to deal with these harmful attacks, there is a need for further research into areas such as how to counteract malicious attacks with healthy noise or how to train an adaptive defense against …

Real-Time Anomaly Detection In Ot Networks Using Gru-Based Autoencoders, Grant Austin Wilkins

Graduate Theses and Dissertations

Operational Technology (OT) networks, particularly those used in critical infrastructure, face increasing cyber threats that target network-level protocols and behaviors. While most anomaly detection research for OT systems has traditionally relied on sensor data, this thesis explores the viability of detecting malicious activity directly from network telemetry. We propose a sequence-to-sequence autoencoder model based on Gated Recurrent Units (GRUs) with multilevel attention, trained to reconstruct normal patterns of packet-level communication extracted from raw PCAP data. The developed feature engineering pipeline integrates general networking attributes such as IP and MAC addresses, ports, and transport protocols with OT-specific protocol information from Modbus …

Improving Image Quality In Electrical Capacitance Tomography Using Otsu Thresholding, Josiah Nombo

Tanzania Journal of Engineering and Technology (TJET)

Electrical Capacitance Tomography (ECT) is an imaging technique used in industrial process monitoring, particularly for monitoring and measuring the composition of multiphase flows. Despite its widespread application, the commonly used Linear Back Projection (LBP) algorithm often produces low-quality images due to its limited ability to handle high permittivity contrasts and nonlinearities. This study investigates the use of Otsu thresholding as a post-processing technique to enhance ECT image quality. By maximizing inter-class variance in the image histogram, Otsu thresholding improves contrast, clarity, and structural definition, enabling more effective segmentation of oil and gas components in multiphase flows. The proposed Otsu-based reconstruction …

Cyber Safety: Protecting Yourself Online, Laxima Niure Kandel, Kayla D. Taylor, Bhawana Poudel, Helen Hernandez

Graduate Student Works

The IEEE Women in Engineering (WIE) Affinity Group of Daytona hosted a cybersecurity awareness event on Saturday, April 26, 2025, at the Port Orange Regional Library. The session was designed specifically to help seniors in Volusia County learn how to protect themselves online, avoid scams, create strong passwords, and safely navigate the digital world.

"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.