Cybersecurity Commons^™

An Evaluation Of Machine Learning Models' Efficacy In Determining Uav Spoofing Attacks, Nicolas Machado, Jaxon Selzer

Discovery Day - Daytona Beach

An Evaluation of Machine Learning Models' Efficacy in Determining UAV Spoofing Attacks - The rapid integration of Unmanned Aerial Vehicles (UAVs) into urban airspace has introduced significant cybersecurity concerns, particularly due to vulnerabilities in Automatic Dependent Surveillance–Broadcast (ADS-B), which lacks authentication and encryption. This project addresses the problem of detecting spoofing and data manipulation attacks that can compromise UAV safety and mission reliability. The objective of this work is to evaluate the effectiveness of machine learning–based anomaly detection, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as protocol-agnostic solutions for identifying anomalous UAV behavior. To achieve this, …

A Survey On Machine Learning Applications For Operating System Fingerprinting, Siri Siqveland

Discovery Day - Daytona Beach

In the modern age of computers and interconnected networks, cybersecurity and cyber-attackers are evolving in tandem to exploit each other’s vulnerabilities. One technique used by both parties is Operating System Fingerprinting (OSF): with the knowledge of what Operating System a target system is running, innate vulnerabilities can be identified and patched or exploited. Historically, OSF utilizes two main methods: passive and active—the former trades accuracy with undetectability while the latter is generally more detectable but more accurate. However, recent work has combined OSF with Machine Learning (ML) to improve accurate identification. The work presented here is a survey for the …

Dcat - Distributed Computing And Analysis Tool, Asher Zwickel, Jacob Burdge

Discovery Day - Daytona Beach

This project uses distributed computing to process and analyze large datasets related to cyber breaches and attacks. Its main goal is to find patterns between initial cyber incidents and what happens next. It looks at whether responses tend to escalate, calm down, or stay about the same over time. Understanding this helps explain how digital conflicts develop and whether they follow predictable paths. The project was built as part of university research and runs on custom software across a cluster of 17 Chromebooks. While the system can study many topics, it is currently focused on cyber activity. The software uses …

Bridging The Gap: Cybersecurity And Occupational Safety Frameworks In Ai Data Centers, Athena Leader

Discovery Day - Daytona Beach

Bridging the Gap: Cybersecurity and Occupational Safety Frameworks in AI Data Centers   As artificial intelligence infrastructure expands, AI data centers represent a critical and underexamined convergence of cybersecurity and occupational safety risk. Existing frameworks such as NIST, OSHA, and ISO standards were largely developed in isolation, leaving significant gaps in how organizations manage risks that are simultaneously digital and physical in nature. This study investigates the gaps and overlaps between cybersecurity and occupational safety frameworks as they apply specifically to AI data center environments. Drawing on a targeted literature review of established regulatory and standards-based frameworks, this research identifies where …

Continuous Authentication For Industrial System Access: Evaluating Bluetooth Low Energy Direction Finding And Channel Sounding For Tailgating And Relay Attacks, Mitchell Mennelle

LSU New Orleans Theses and Dissertations

In physical access control, authentication is often viewed as a one-time event, where, once an authorized user crosses a protected boundary, downstream systems assume the user remains physically present. Tailgating and relay attacks violate this assumption. In this thesis we propose a continuous authentication layer based on two Bluetooth Low Energy spatial signals. Angle of Arrival direction finding follows the trail of a worn credential to determine when an operator exits a work zone. Bluetooth Channel Sounding measures a physical property of the radio path and verifies distance during stationary periods. Limiting Relay Attacks with Event-Driven Distance Verification. A stream …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Surveyception: An Exploration Of Deceptive Survey Forms, Muhammad Danish

Computer Science ETDs

Survey platforms such as Google Forms and Microsoft Forms are widely used for feedback, data collection, and engagement, but scammers increasingly exploit them to distribute phishing and deceptive attacks. This thesis presents a large-scale study of survey-form abuse across ten major providers. We collected 140,000 forms from three sources: public posts on X, search-engine results, and web pages from the top 10 million DomCop-ranked domains. Using automated filtering and manual qualitative review, we identified 2,645 forms requesting sensitive information and classified 566 as scams. These forms used techniques including phishing, private-secret theft, account and personal-data harvesting, financial deception, and psychological …

Escaping The Cyberstorm: A Gamified Social Engineering Training Program, Noah Mcclanahan, Fadi Abu-Amara, Ali Khattab, Travis Jett, Andre Jackson

Journal of Cybersecurity Education, Research and Practice

In this research work, we explored the effectiveness of gamification in improving cybersecurity awareness and training users on targeted social engineering attacks. Traditional cybersecurity training focuses on lectures and videos. These training methods may not actively engage employees, which reduces their knowledge retention and ability to recognize social engineering attacks. This lack of involvement is a concern, as social engineering continues to be one of the most prevalent attack methods faced by end-users. A gamified training program, Escaping the Cyberstorm, was developed using the Godot game engine to address key challenges in spreading cybersecurity awareness. The game includes real-life …

Stop Blaming My Users: Illumination Of The Technocentric Mythos Bias, Ervin H. Frenzel, Richard Lightcap

Journal of Cybersecurity Education, Research and Practice

 Abstract -This conceptual essay addresses the need for systemic and systematic transdisciplinary analytical techniques within cybersecurity and technical security. This conceptual essay is contingent upon recognition that cybersecurity is not simply technical in nature, it does not need an adversary, and more importantly it is based upon systems engineering and systems thinking.  The essay contributes a socio-technical attribution chain and field-specific ontology/taxonomy which distinguish user-triggered events from root causes, latent conditions, technical debt, validation failures, governance failures, and attribution bias before assigning responsibility to end users. It systematically defines an ontology inclusive of developer technical debt, organizational debt arising from …

Assessment And Evidence Practices In Cybersecurity Education: A Systematic Review (2015–2025), James K. Mayberry

Journal of Cybersecurity Education, Research and Practice

This study presents a PRISMA-based systematic review of 412 cybersecurity education intervention studies, coding assessment methods, evidence types, claimed outcomes, use of established assessment instruments, and artifact availability. Despite frequent claims of skill development and workforce preparation, 45.4% of studies reported no identifiable assessment. Knowledge tests appeared in 11.4% of studies, while performance assessments appeared in 10.2%. From 2015 to 2025, assessment practices remained dominated by post-only designs or no assessment, with no statistically detectable increase in pre/post-capable designs. Use of established assessment instruments was rare, with 94.2% of assessed studies using ad hoc measures or not identifying an established …

Assessing Flaws In Captcha Security Through Progress In Ai, Jaydon Stanislowski

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Protecting the internet from the threat of malicious bot activity is an important problem as AI tools become more powerful and commonplace over time. To that end, security measures are employed across websites in the form of CAPTCHAs, short challenges designed to identify and block fake web traffic. Yet, they become less effective over time as AI becomes more powerful, and thus more capable of solving them. This paper examines recent research on the threat to CAPTCHA security posed by current AI models and how this security can be reinforced over time, focusing primarily on Google’s reCAPTCHA v3.

Zero Trust Architecture And Ransomware Mitigation, Ely Johnson

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Ransomware has become a critical threat to modern enterprises, exploiting excessive privileges and flat network architectures to spread rapidly. Traditional perimeter-based security models are insufficient, as they rely on implicit trust within internal networks. This paper examines how Zero Trust Architecture (ZTA) mitigates ransomware through least privilege access, continuous monitoring, and micro- segmentation. Experimental results show that ZTA can significantly reduce impact, limiting encryption to about 20% of targeted files while preserving most data. Continuous monitoring enables rapid detection (5.3 seconds) with high accuracy (up to 97.2%) and a 78% reduction in false positives. Micro-segmentation further restricts lateral movement, reducing …

Security Limitations Of The Can Bus And Detection Through Power Fingerprinting, Ken Broden

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper examines the vulnerabilities of the Controller Area Network (CAN), the standard communication protocol used in most modern vehicles. It explains why CAN is widely adopted and outlines key security weaknesses in its design. The paper then reviews recent research efforts to detect and mitigate these vulnerabilities, with particular focus on an approach to origin authentication that relies on the unique power consumption patterns of each individual electronic control unit on a CAN bus.

Geospatial Governance Failures In The Department Of Defense: Contractor Noncompliance, Ai Adoption, And The Parallel Treatment Of Gis And Cybersecurity, Lyndsey Olmstead

Geography and the Environment: Graduate Student Capstones

The Department of Defense's treatment of geographic information systems and cybersecurity as parallel rather than integrated policy domains produces geographically predictable vulnerability patterns across its global military installation footprint. This capstone investigates that conclusion through original spatial analysis, constructing a five-variable composite geospatial vulnerability index across the six U.S. Combatant Command regions using publicly available unclassified data. EUCOM ranked highest overall, driven by GPS/PNT spoofing density, commercial satellite coverage, and cyber incident frequency; CENTCOM ranked second, driven by OSINT exposure incidents and governance risk. The null hypothesis of random geographic distribution is rejected. Findings confirm the structural governance gap documented …

Residential Ai Data Centers: Security, Privacy, And Governance Concerns, Alan Saquella

Publications

The concept of placing mini data centers and distributed AI computer nodes inside residential homes may appear innovative from an energy efficiency perspective, but it introduces significant security, privacy, governance, and liability concerns. What is effectively occurring is the expansion of commercial and potentially critical infrastructure into lightly protected residential environments.

Once a residence becomes part of a distributed computer grid supporting hyper-scalers, AI providers, or enterprise workloads, the home is no longer simply a private residence. It becomes a commercial technology asset, a potential cyber target, and even a physical target. A distributed network of thousands of residential nodes …

A Systematic Review Of Intrusion Detection Systems For Internet Of Medical Things: Performance, Efficiency, Explainability, And Generalization, Oswald Adohinzin, Youssef Harrath

Research & Publications

The Internet of Medical Things (IoMT) has transformed health care delivery through medical devices, remote patient monitoring, and real-time clinical decision support. However, the proliferation of IoMT devices introduces security vulnerabilities that put patient safety and data privacy at risk. Intrusion Detection Systems (IDS) have emerged as essential components for protecting IoMT networks from cyberattacks. This article presents a systematic review of IoMT-IDS research, analyzing 53 high-quality papers published between 2020 and 2025, identified through database searches spanning 2016–2025 across IEEE Xplore, Springer, ScienceDirect, and ACM Digital Library. We organize the literature through a comprehensive taxonomy spanning classical machine learning …

Adversarial Robustness Of Perceptual Hashing Systems: A Unified Security Evaluation Framework, Avijit Roy

Student Theses

Social network platforms, child safety organizations, and image provenance systems use perceptual hashing to identify known child sexual abuse material (CSAM), support content moderation and reverse image search, and verify image integrity. Perceptual hashing works by producing similar fingerprints for visually similar images, even after common transformations such as compression, resizing, or minor brightness changes. This useful similarity-preserving property also creates an adversarial attack surface, as attackers can use AI-assisted or conventional image manipulation techniques to move a hash across a matching threshold while maintaining visual similarity, often without access to specialized hardware.

The security failures produced by adversarial attacks …

The Security Of Llm-Generated Code, Christopher Brian Gonzalez Ayala

Student Theses

The rapid adoption of Large Language Models (LLMs) in software development has transformed coding practices by enabling automated code generation, completion, and optimization. Despite these advantages, concerns persist regarding the security and reliability of LLM-generated code. This study presents a comprehensive evaluation of both the functional correctness and security of code produced by three prominent LLMs as of early 2026. A total of 4,800 code snippets were generated using 100 security-focused programming prompts derived from the OWASP Top 10:2025, translated across eight natural languages and two phrasing styles (literal and natural developer-oriented prompts). To assess performance, a multi-stage experimental framework …

2026 Cyber-Resilient Health Care Workshop Report, Malcolm Schongalla, Sergey Bratus

Computer Science Technical Reports

The ISTS and the Dartmouth College Cybersecurity Cluster hosted the successful, inaugural Cyber-Resilient Health Care (CRHC) Workshop, March 5th & 6th, 2026. The event theme was "Innovation and Implementation," in response to the need to shift from reactive to proactive resiliency measures in the healthcare sector. Approximately 30 experts in clinical health care, cybersecurity, medical technology, policy, and innovation met to discuss solution-focused innovations addressing hard, cyber-related problems in health care. The agenda featured keynotes, an expert panel, innovation pitches, small group discussions, and a tabletop infrastructure disaster exercise. Participants gained insights into the obstacles and solutions involved in supporting …

Anonymity And Accountability In Secure Messaging, Erin Kenney

Dissertations

Encypted messaging has become more and more prevalent as time moves on, and its benefits in assuring privacy cannot be overstated, but it also brings along with it concerns on how to moderate platforms where all messages are hidden. Message Franking, followed by Traceback systems, addressed these concerns by allowing the sender of a message to be proven when reported, even for forwarded messages in the case of Traceback, however these systems damage the privacy guarantees that originally motivated encrypted messaging to begin with.

In practice, even without those concerns encrypted messaging alone is not enough to prevent the most …