Cybersecurity Commons^™

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 …

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 …

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 …

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 …

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 …

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 …

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.

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 …

Romance Scam Reporting And Support: Help-Seeking Timing, Trust, And Escalation, Ld Herrera

Research & Publications

Built on social engineering and identity deception, romance scams often create financial loss and distress. For victims, it can be difficult to know where to go, what information is needed, and what outcomes are realistic. This paper reports results from an anonymous survey of people who were targeted by or experienced a romance scam (completed surveys: n=386), focusing on (1) when and whether victims first reach out for help, (2) perceived difficulty and confidence in navigating support, (3) how trust relates to expectations of assistance, and (4) how loss severity relates to transfer-method complexity. When help was sought, it was …

A Strategic Roadmap For Assessing And Educating On Personal Cybersecurity Practices In Universities*, Ryan Lopez, Ysani Peña

Campus Research Month

Universities face a common cybersecurity threat: their own users. Although organizations may meet compliance standards and implement robust security infrastructures, the individual user remains the weakest link. This is particularly evident in higher education institutions, where both students and employees are frequent targets of cyber threats due to a lack of cybersecurity awareness. This paper proposes a strategic roadmap for assessing university student bodies and employee populations through cybersecurity domains that directly affect personal cyber hygiene awareness and practice.

Our proposed roadmap was validated in a U.S. university by using a domain-focused survey and simulated phishing campaigns. After the identification …

Foxbuddy, Luis Eduardo Garza Jr.

Posters - 2026

Emergency preparedness remains a significant challenge for individuals due to disorganized resource management and lack of accessible guidance. Additionally, cybersecurity risks during emergencies are often overlooked, leaving individuals vulnerable to digital threats such as phishing, scams, and data exposure. FoxBuddy provides a centralized, user-friendly platform that enhances both physical preparedness and cybersecurity awareness.

A.I.R.E., Laurene Robinson

Presentations - 2026

•Cybersecurity analysts rely on reverse engineering to understand suspicious software. •Ghidra can surface decompiled code, but it does not fully explain function purpose, behavioral meaning, or analyst priority. •When symbols are stripped and context is weak, analysts must still reconstruct intent manually from low-level output. •That process is Time-consuming , complex and , operationally costly

A.I.R.E. - Ai-Assisted Reverse Engineering, Laurene Robinson

Posters - 2026

Reverse engineering plays a vital role in cybersecurity by helping analysts examine unknown binaries, investigate malware, identify vulnerabilities, and better protect sensitive systems. However, once a program is compiled and stripped, the meaningful names that describe its behavior are lost, leaving behind generic function labels like FUN_00401a30. Analysts must then manually interpret decompiled code, trace call chains, and infer program behavior function by function, which is slow and mentally demanding on large binaries. To address this challenge, this project introduces A.I.R.E., a local Ghidra extension that extracts contextual evidence from stripped functions and uses a locally hosted language model to …

Next-Generation Democratic Cyber Statecraft - Balancing The Signal: Shutdown Shocks And Democratic Digital Governance, Scott M. Di Panni

School of Public Policy Capstones

This paper develops Next-Generation Democratic Cyber Statecraft (NG-DCS), a unified strategic doctrine for democratic governments to contest the cognitive domain against authoritarian adversaries. Drawing on twenty-six years of cross-national panel data (1999–2024) spanning 213 countries, game-theoretic modeling, and qualitative case analysis, the paper establishes three interconnected empirical and theoretical foundations. First, cross-national OLS regression across 160+ countries demonstrates that regime type is the dominant structural determinant of internet freedom (R²=0.615, β=2.513, p< 0.001), explaining more than twice the variance attributable to per-capita wealth (R²=0.268). Democratic governance, not economic development, produces open digital environments. Second, a two-way fixed effects (TWFE) difference-in-differences study exploiting government-ordered internet shutdowns as discrete policy interventions finds that digital restrictions causally degrade V-Dem governance quality by 0.21–0.38 standard deviations (p< 0.001 across all specifications). Treatment effects are immediate (β=−0.302 at k=0) and persist through five post-treatment years (β=−0.246 at k=+5), indicating structural rather than transitory governance damage. Parallel trends validation (p=0.352) and Callaway–Sant’Anna heterogeneity-robust estimation (ATT=−0.230, SE=0.077) support causal identification. Instrumental variable triangulation (2SLS β=−0.949, p=0.005) confirms that simultaneity was attenuating, not inflating, the primary estimates. Third, formal game-theoretic analysis reveals that the current U.S.–adversary equilibrium is (Restrain, Escalate)—the risk-dominant but Pareto-inferior outcome of a Stag Hunt structure. China, Russia, North Korea, and Venezuela each occupy structurally distinct positions (Stackelberg commitment, asymmetric two-level, autarky, and reactive trigger, respectively), requiring differentiated doctrinal responses rather than a uniform strategic playbook. Generative AI and algorithmic governance are shown to accelerate cognitive vulnerability by collapsing influence operation costs and exploiting engagement-optimized platform architectures that systematically degrade deliberative capacity in democratic populations.

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Exploring The U.S. Public Service Employees’ Experiences With Gamified Cybersecurity Awareness Training, Bisola Adepoju

Human Resource Development Theses and Dissertations

The purpose of this qualitative study was to explore the U.S. public service employees' experiences with gamified cybersecurity awareness training. The primary research question guiding this inquiry was: How do U.S. public service employees make sense of their lived experiences with gamified cybersecurity awareness training? Two secondary questions examined what aspects of the training employees perceive as meaningful or disengaging, and how they interpret their motivation, engagement, and cybersecurity awareness during training. I chose an interpretive qualitative research design grounded in a constructivist paradigm and purposefully selected 11 U.S. public service employees who had participated in gamified cybersecurity awareness training. …

Pong Revised: Network-Based Competitions Through Secure Socket Services, Noah T. Jennings, Destiny D. Hale, Jared D. Williams, Michael J. Lively-Scholz

Knowledge and Creativity Expo

We aim to provide a safe, thrilling, locally hosted, and educational multiplayer experience that can be quickly replicated in modern Capture The Flag (CTF) events.

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Recovery And Validation Of Fragmented Rar Files Through The Scalpel3 Application, Thomas L. Landaiche Iii

LSU Master's Theses

A core component of filesystems includes an address table or other means of tracking metadata on where a given file resides within a disk image. This is crucial for regular operation of a computer, or disk analysis in digital forensics. When filesystem information is missing or corrupted, locating saved files on a disk becomes challenging. Whether the disk was accidentally wiped or intentionally tampered with, data still present on the disk, even while untracked, can possibly be recovered beyond what the filesystem registers. File carving in digital forensics is important for these scenarios when data recovery is necessary but address …