Cybersecurity Commons^™

Quantum Readiness In Cybersecurity Education: A Framework For Preparing The Next Generation In The Post-Quantum Era, George Antoniou

Faculty and Staff Publications & Presentations

The rapid advancement of quantum computing represents both a revolutionary opportunity and an existential threat to contemporary cybersecurity infrastructure. While quantum computers promise unprecedented computational capabilities, they simultaneously pose a critical risk to current cryptographic protocols that protect sensitive data, financial systems, and national security frameworks. Post-quantum cryptography (PQC) standards, recently formalized by NIST in 2024, provide a roadmap for quantum-resistant encryption. However, a significant gap exists between technological advancement and educational preparedness, with most cybersecurity curricula failing to adequately prepare students for the quantum era. This paper addresses the urgent need for comprehensive quantum readiness in cybersecurity education across …

A Scalable Cybersecurity Model For Academic Makerspaces, William Faircloth

Cybersecurity Undergraduate Research Showcase

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

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

Cybersecurity Undergraduate Research Showcase

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

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

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

Cv: Mathias Plass (Cybersecurity), Mathias Plass

ECaMS Department Faculty Curricula Vitae

No abstract provided.

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

Cybersecurity Undergraduate Research Showcase

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

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

Cybersecurity Undergraduate Research Showcase

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

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

Cybersecurity Undergraduate Research Showcase

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

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

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

Cybersecurity Undergraduate Research Showcase

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

Systematic Review Of Elementary Cybersecurity Education: Curriculum, Pedagogy, And Barriers, Na Liu, Siyu Long, Florence Martin

Journal of Cybersecurity Education, Research and Practice

Abstract -As children increasingly engage with digital platforms, the need for effective cybersecurity education has become urgent. This systematic review synthesizes 81 studies published between 2017 and 2024 to examine global curricula research focus and topics, pedagogical approaches and assessment methods, and key challenges in elementary cybersecurity education. The findings reveal six major thematic categories: student awareness, parental mediation, teacher engagement, curriculum design, community and policy support, and pedagogical innovation. Among instructional strategies, game-based learning and narrative storytelling emerge as the most frequently explored. Despite this growth, major gaps remain in curriculum consistency, teacher preparation, assessment rigor, and stakeholder coordination. …

Effects Of Code Scaffolding In Increasing Student Confidence In Programming Cryptography, John Denny

LSU Master's Theses

Cryptography is essential for secure communications, and new threats require more students willing to program and interact with cryptographic systems. Previous research is focused on tools for teaching these systems at a high level, teaching through attacks against these systems, and proper use of these systems in software development. In this paper, we seek to design a workshop to use scaffolded Python code to teach how these cryp- tographic systems are designed. We explore the use of code scaffolding for students to program an example implementation of the McEliece crypto- graphic system to build confidence in working with these systems. …

Exploring Runtime Evolution In Android: A Cross-Version Analysis And Its Implications For Memory Forensics., Babangida Bappah

LSU Master's Theses

Userland memory forensics has become a critical component of smartphone investigations and incident response, enabling the recovery of volatile evidence such as deleted messages from end-to-end encrypted apps and cryptocurrency transactions. However, these forensics tools, particularly on Android, face significant challenges in adapting to different versions and maintaining reliability over time due to the constant evolution of low-level structures critical for evidence recovery and reconstruction. Structural changes, ranging from simple offset modifications to complete architectural redesigns, pose substantial maintenance and adaptability issues for forensic tools that rely on precise structure interpretation. Thus, this paper presents the first systematic study of …

Enhancing Cyberattack Resiliency Through The Radiotherapy Backup And Recovery Dashboard Tool, Justin Pijanowski, Eric Nguyen, Yasin Abdulkadir, Justin Hink, Yevgeniy Vinogradskiy, James Lamb

Department of Radiation Oncology Faculty Papers

PURPOSE: Radiation Oncology departments impacted by recent cyberattacks were unable to access data backups or their Record and Verify (R&V) system and therefore faced challenges to resume patient treatments in a timely manner. We present a novel software tool that backs-up critical radiotherapy treatment information and displays essential information for on-treatment patients in an intuitive and accessible dashboard allowing clinics to continue radiotherapy treatments. The purpose of this report is to describe implementation details, challenges, and share open-source code to facilitate radiation oncology clinics' efforts to develop tools to improve cyberattack resiliency.

METHODS: The Radiotherapy Backup and Recovery Dashboard Tool …

Teaching Cybersecurity And Ai Across Borders: From Foundations To Ethics, George Antoniou

Faculty and Staff Publications & Presentations

This presentation examines how interdisciplinary course design in AI and cybersecurity can expand undergraduate research while directly supporting career readiness. At Lynn University, the Foundations of Cybersecurity & AI course was updated to serve as the entry point for both Cybersecurity and Data Analytics majors. The course integrates case studies, digital forensics and cloud security labs, and applied exercises with AI-enabled defense-in-depth strategies. Students build core technical competencies while engaging in course-based research that mirrors industry practice. As part of a Fulbright grant, a complementary course, AI & Ethics, was developed for the University of Tirana. Proposed as a mandatory …

Securing The Digital Harvest: Cybersecurity As A Core Agribusiness Skill, Jody Herchenbach, George Grispos

Mountain Plains Business Conference

The digitization of agriculture, through IoT-enabled equipment, cloud platforms, and precision technologies, has improved efficiency and profitability while also introducing significant cybersecurity risks. These vulnerabilities can disrupt supply chains, compromise sensitive data, and undermine financial stability. Yet agribusiness degree programs often overlook cybersecurity education. This paper proposes integrating cybersecurity content on threat awareness, incident response, and data protection into agribusiness curricula. Embedding these elements equips graduates to manage both digital and financial risks, enhancing resilience and competitiveness. Such curricular innovation aligns technical and managerial training, preparing future agribusiness professionals to lead securely and sustainably in an increasingly connected industry.

Ecu-Malnett, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

Optimizing Cybersecurity Through Ai Predictive Analytics And Human Expertise, Cathy Mae C. Dutong

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

As cybersecurity threats evolve in complexity and scale, the reliance on artificial intelligence (AI) has become increasingly prevalent across both public and private sectors. This study examines the dual role of AI-driven predictive analytics in strengthening organizational cybersecurity, while addressing the ongoing need for human oversight. Through a mixed-method approach, combining survey data from cybersecurity professionals with an extensive literature review, this research analyzes AI's capacity to detect emerging threats, the systemic challenges associated with AI integration, and the indispensable role of human expertise in interpreting AI outputs. Findings indicate that while AI enhances proactive …

Microarchitectural Malware Detection Via Translation Lookaside Buffer (Tlb) Events, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose R. Gutierrez Del Arroyo, Scott R. Graham

Faculty Publications

Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to malware classification using only TLB-related Hardware Performance Counters (HPCs), explicitly excluding any dependence on timing features such as task execution duration or memory access timing. Our methodology evaluates whether TLB data alone, without any timing information, can effectively distinguish between malicious and benign programs. We test this across three classification scenarios: (1) A binary classification problem involving distinguishing malicious from benign tasks, (2) a …

Cybersecurity And Intention To Use Mobile Banking Applications, Ishmael Chikoo, Salah Kabanda

African Conference on Information Systems and Technology

The adoption rate of mobile banking amongst consumers remains low, especially in developing countries where there is a knowledge gap in understanding why consumers do not engage in the frequent use of mobile banking applications. Given that most financial institutions see mobile banking as a strategy for their competitive advantage; it is important that they understand how best to address consumer’s fears brought about by cybersecurity threats. The purpose of this study is to investigate the perceived influence of cybersecurity on the user’s intentions to use mobile banking applications. Data collected from 90 participants was statistically analysed in Smart PLS …

Machine Learning And Crime Prevention, Emily Lizewski

Student Theses

Predictive policing uses machine learning to analyze crime patterns and help law enforcement better efficient use their resources. These tools can improve accuracy by highlighting complex trends in large sets of data. While this technology has its advantages, it also raises important ethical and social questions. Within this paper we looks at how predictive policing works, focusing on the machine learning models often used such as decision trees, random forests, gradient boosting, and models that factor in both time and location. It also explores how these tools might unintentionally reinforce biases already present in historical crime data. In reviewing the …