Cybersecurity Commons^™

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

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 …

Meeting The Moment With Ai-Employer Informed Education, Brent Terwilliger, John Faraca

Publications

As artificial intelligence transforms aviation, aerospace, and autonomy-related sectors, higher education must adapt to meet evolving workforce demands. This session shares emerging findings from a nationwide study led by Embry-Riddle Aeronautical University, focused on employer perceptions of AI adoption, responsible use, and workforce preparedness in domains including uncrewed systems, space systems, robotics, and advanced air mobility. Based on a structured survey and follow-up interviews, the presentation explores how organizations are using AI tools, from generative platforms to enterprise systems, and defining effective and inappropriate use in operational contexts. Participants will gain insight into critical concerns (e.g., data privacy, compliance, security, …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

Developing A Framework For Microchip Design Recovery, Eric Diep

Graduate Theses and Dissertations (2019 - present)

Due to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research develops a methodology for microchip design recovery, seeking to validate and reproduce prior approaches to physical reverse engineering using low-cost tools and techniques. We used mechanical hardware abrasion tools and techniques to delayer and capture silicon integrated chip (IC) layout. We focused on the Mifare Classic EVl microchip, commonly implemented in public transit/transportation cards, to extract information for design recovery. The research explores limitations and advantages of mechanical abrasion and optical microscopy in context to modem chip …

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 …

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 …

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 …

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 …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …

Proof Of Recovery: A Model To Enhance Data Integrity In Data Management Systems, Gustaf Barkstrom

Theses and Dissertations

Businesses lose millions of dollars every year when they can’t restore data from backups. Research shows that Disaster Recovery Plan (DRP) testing is not conducted frequently enough, nor are records maintained that demonstrate full data recovery from backups. This work introduces a design science artifact called PRTOK that aims to increase DRP testing. The design science artifact is a software solution that integrates with Data Management Systems (DMS)

such as iRODS and DSpace, and can work with formats such as HDF5 and BagIt. Proof-of- recovery records, or tokens, are recorded in a replicated, resilient, and indelible proof-of- authority blockchain data …

Viability Of Widely Used Encryption Schemes In Drone Transmission, Emanuel Yasir Nelson

Cybersecurity Undergraduate Research Showcase

This paper presents throughout research on the security issues related to drone transmission. These topics were addressed and explained, in particular the aspects relating to cybersecurity, for utmost clarity. These include threats and vulnerabilities, drone transmission the impact of encryption on latency, and the details of the encryption methods AES-128, AES-256, and ChaCha20 that were used in the experiment described in the paper. Each encryption method performance was measured and outputted by the Python code developed and used in the experiment. Afterwards, the performance of each method was analyzed in relation to their decryption time, encryption time, end to end …

Confidential, Attestable, And Efficient Inter-Cvm Communication With Arm Cca, Sina Abdollahi, Amir Al Sadi, Marios Kogias, Hamed Haddadi, David Kotz

Other Faculty Materials

Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation guarantees, existing CVM architectures lack first-class mechanisms for inter-CVM data sharing due to their disjoint memory model, making inter-CVM data exchange a performance bottleneck in compartmentalized or collaborative multi-CVM systems. Under this model, a CVM's accessible memory is either shared with the hypervisor or protected from both the hypervisor and all other CVMs. This design simplifies reasoning about memory ownership; however, it fundamentally precludes plaintext data sharing between CVMs because all inter-CVM communication must pass through hypervisor-accessible …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Low-Level Memory Attacks On Edge Assisted Robotic Applications, William Arnold

Master of Engineering Theses

This thesis investigates how low-level memory faults can undermine edge-assisted robotic systems that rely on memory optimization. As robots are utilized in real world applications, the ability to operate safely and successfully in mission critical deployment becomes important. To help achieve these goals, developers are increasingly starting to place computation nodes at network edges to meet latency and reliability requirements. Edge nodes, however, are resource-constrained and resources conservation techniques such as Kernel Same-page Merging (KSM) are enabled to deduplicate identical pages across processes or virtual machines. This thesis shows that this optimization technique quietly widens the attack surface and can …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

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 …

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 …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks, Michael A. Eddy

Theses and Dissertations

This research evaluates the impact of electronic warfare, particularly jamming, on an audio-based drone detection wireless sensor network (WSN) using Monte Carlo simulations. A six-node IEEE 802.15.4 network, with five edge nodes and a central sink, is tested against jamming probabilities ranging from 0-100% in 5% increments across 30 iterations per configuration. Results show that packet delivery ratio (PDR) degrades linearly at approximately 20% per jammed node, while detection performance often exceeds PDR. Even at 80% jamming, detection success rates remain above 57%, highlighting resilience despite network degradation. The study reveals that jamming effectiveness depends on node placement relative to …

Protocol Transformations Across Osi Network Stack Layers For Attack, Evasion, And Defense, Nathan Tusing

All Dissertations

Network endpoints frequently contend with errors and deviations within protocols. Many factors account for these deviations including noise, tampering, and algorithm implementations. Intermediate nodes are expected to modify instantiated protocols and not guarantee correctness. This ability to modify traffic enables all sides of network security to alter security and performance properties of protocols, and we define this intermediary modification of an instantiated protocol as a transformation. Protocol transformations traverse layers of the OSI reference model and changes a protocol's time series byte sequence. Within this thesis, we show that this framework applies to multiple domains and protocols. Common examples of …

Dynamic Key-Based Privacy-Preserving Authentication Scheme For Internet Of Drones, Zain Chaudhary

Honors Theses

The Internet of Drones (IoD) proliferation has catalyzed transformative changes across various industries, from agriculture to urban management. However, expanding drone networks also presents significant security challenges concerning secure communication and authentication. This paper introduces a robust privacy-preserving key-based authentication scheme tailored explicitly for the IoD, utilizing a matrix key generated by Hierarchical Message Authentication Codes (HMAC) and the SHA-256 algorithm to address these vulnerabilities. Our system enhances security by ensuring each drone in the network can authenticate securely and reliably with a central unit, preventing unauthorized access and securing communications against common threats like eavesdropping and impersonation attacks. Our …

Security Vulnerabilities In Mobile Operating Systems Used In Iot Devices: An Examination Of Current Challenges And Countermeasures, Isain Cortes Jr.

Cybersecurity Undergraduate Research Showcase

No abstract provided.

Real Time Pii Scanning, John David

Electronic Theses and Dissertations

The increased amount of web applications and internet software solutions utilizing cloud frameworks has contributed to large data sets of system log messages being generated constantly. These messages may contain sensitive data, creating an additional security risk for the systems and contributing to the need for analysis of such large volumes of data in real time. Large commercial data monitoring systems can solve for these analysis requirements, but they can be costly. We present a solution to analyzing web application log data which ingests it, processes it and visualizes sensitive data found within in real time. Our solution utilizes an …

How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez

Journal of Computer Science Integration

This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …

Broadening Participation Of Teachers In Computing: Examining Postsecondary Educational Experiences And Prospective Educators’ Cs Teaching Interests, Robert Schwarzhaupt, Alexsandra Galanis, Joanna Goode, Kate Blanchard, Jill Bowdon, Joseph P. Wilson

Journal of Computer Science Integration

Teacher shortages in K–12 computer science (CS) education negatively impact students’ access to CS courses, exposure to CS concepts, and interest in CS-related careers. To address CS teacher shortages, this study seeks to understand factors related to expressing a preference to teach CS among prospective teachers. The study team analyzed data from 27,700 prospective teacher applications accepted into the 2016–2020 Teach For America (TFA) corps (cohorts). The TFA corps is an alternative teacher development program that recruits and prepares participants to obtain their teaching certification while they work for at least two years in underserved communities on a temporary teaching …