Computer Sciences Commons^™

Application Of Multi-Criteria Decision Analysis For Quantifying Responsibility In Ml-Based Intrusion Detection System, Mona Mohamed, Zekra Sakr

Neutrosophic Systems with Applications

With the swift growth in Internet of Things (IoT), certifying secure and trustworthy networks has turned to be a critical challenge, particularly as IoT devices are increasingly vulnerable to sophisticated cyberattacks. As a remedy, intelligent intrusion detection systems (IDS) evolved as promising solutions in recent years, but deciding on the appropriate model remains difficult because of competing performance and trustworthiness criteria. To this end, this paper explores a novel application of an ML-augmented decision-making framework to enhance security-related decision-making in IoT environments. The framework systematically evaluates and ranks ML-based IDS systems according to different evaluation criteria with distinct trade-offs, including …

Exploitation For All: The Factors That Enable Pig Butchering Schemes To Weaponize Cybersecurity's Weakest Link, Melaney Freeman

Boise State Graduate Student Projects

This paper discusses pig butchering schemes and how they leverage human weaknesses through advanced social engineering and manipulation techniques that are enabled through the use of human trafficking, forced labor, and government corruption in Southeast Asia. Details regarding scammer exploitation and the formation of a victim-offender identity is presented, followed by the explanation of factors that allow organized crime groups to exist. Essential tactics used by scammers are examined to understand how they weaponize people's vulnerabilities for the duration of the scam to build a relationship with the victim and earn their trust in order to financially exhaust them through …

Broadband Resilience By Zero Trust Community Network Policy Design, Lee W. Mcknight, Danielle Smith

The Lender Center for Social Justice

This paper proposes a Zero Trust framework for broadband policy design to enhance community network resilience. It provides a governance and policy perspective for ensuring secure, equitable broadband access.

Digital Forensics And Ai: Artifact Analysis And Using Ai In The Forensics Domain, Clinton Joel Walker

LSU Doctoral Dissertations

Digital Forensics (DF) is a field of forensic science focusing on the acquisition, authentication, and analysis of digital evidence while maintaining integrity of that data. DF analysts use forensic tools to parse large volumes of data for investigations and depend on them for identification of pertinent digital evidence in vast amounts of data. Keeping up with innovations and ever-expanding data volumes is a constant challenge for these investigators. The prevalence of Artificial Intelligence (AI) in everyday computing is rapidly expanding, with the use of Machine Learning (ML) and Large Language Models (LLM)s becoming increasingly commonplace. Innovations in technology bring new …

Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related HPCs for multi-task classification, without incorporating …

Enhancing Cybersecurity Based On Blockchain Technology: A Systematic Review, Sarah Mohammed Shareef, Rehab Flaih Hassan

Journal of Soft Computing and Computer Applications

Cybersecurity is a crucial component of the security system that guards against unauthorized access to digital transactions. Blockchain is a decentralized ledger used to securely exchange digital currencies and conduct trades and transactions. Blockchain technology has led to significant changes in electronic transactions. The enormous potential is being exploited in many areas such as financial services, real estate, supply chain, and the Internet of Things. Despite being a security system, it has suffered from security threats to sensitive data. Phishing and 51% attacks can circumvent blockchain security, highlighting the need for thorough user education and awareness. Additionally, blockchains based on …

A Survey On Immersive Cyber Situational Awareness Systems, Hussain Ahmad, Faheem Ullah, Rehan Jafri

All Works

Cyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. Moreover, conventional forms of human–computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

Cybersecurity And Global Threats: A Comparative Analysis Of Estonia And Russia’S Policies, María Paula Morales Palacios

The Confluence

As digital technology continues to reshape the foundations of modern life, the question of how states respond to cyber threats has become increasingly urgent. This paper examines how political systems shape national cybersecurity policies by comparing Estonia and Russia, two countries facing similar external threats but governed by vastly different structures. Estonia’s democratic framework emphasizes transparency, citizen participation, and international cooperation, while Russia’s semi-authoritarian model centers on state sovereignty, centralized control, and strategic offensive capabilities. Drawing on key historical events, including the 2007 cyberattacks on Estonia and the 2016 attacks on Russian banks, the paper explores how each state’s political …

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 …

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 …

Automation Of Vulnerability And Patch Management: Information Extraction, Association, And Optimization, Kylie Mcclanahan

Graduate Theses and Dissertations

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout …

How Do Simulated Phishing Attacks Impact Cybersecurity Awareness And The Enhancement Of Security Protocols Among Faculty Members In A University Environment?, Navnoor Sandhu

University Honors Program Senior Projects

Phishing attacks are cyber threats where attackers deceive users into performing actions that compromise the user’s security and benefit the attacker. In 2024 alone, phishing attacks have resulted in estimated damages of around 800 million dollars [1]. In response, many institutions have implemented internal simulated phishing attacks to enhance their employees' cybersecurity awareness. This training exercise has been proven beneficial in improving cybersecurity awareness on an enterprise scale[4]. This study aims to evaluate the potential effectiveness of a simulated phishing attack within a university setting, which is a relatively unseen practice thus far. Universities, like other secure organizations, store sensitive …

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 …

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 …

A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari

School of Computing: Dissertations, Theses, and Student Research

The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …

From Text To Utility: Distance-Aware Contrastive Learning For Detection-Ready And Shareable Malware Descriptions, Ivan Alejandro Montoya Sanchez

Open Access Theses & Dissertations

The rapid rise of sophisticated malware variants poses significant challenges for cybersecurity analysts, particularly due to the scarcity of data on newly emerging threats. Due to privacy, legal, and operational constraints, malware samples are often not shareable; instead, organizations publish cyber threat intelligence (CTI) in natural language. However, these reports are typically unstructured and inconsistent, limiting their utility in machine learning (ML) models. This thesis explores whether high-fidelity, shareable threat intelligence can be automatically generated from structured malware behaviors to supplement ML models when direct access to malware samples is limited. Two central questions are addressed:(i) How can descriptions be …

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 …

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.

Systemization Of Knowledge (Sok): Goals, Coverage, And Evaluation In Cybersecurity And Privacy Games, Yue Huang, Marthie Grobler, Lauren S. Ferro, Georgia Psaroulis, Sanchari Das, Jing Wei, Helge Janicke

Research outputs 2022 to 2026

This paper systematized existing knowledge on cybersecurity and privacy game-based approaches, exploring their goals, scope, and evaluation methods. Our review of 93 academic papers revealed that these approaches serve multiple purposes and target diverse player types. We identified 11 key aspects of cybersecurity and privacy that these approaches addressed, such as threats, defensive strategies, and data privacy. Additionally, we analyzed the effectiveness evaluation methods of these approaches, emphasizing the connections between evaluation techniques, types of data used, and their alignment with the approaches' goals. We also summarized the aspects of user experience evaluated in the literature and the types of …

Using Generative Artificial Intelligence To Improve Software-Defined Network Security: A Brief Survey, Anthony Smith

Honors Theses

This paper aims to explore various approaches to using generative artificial intelligence (GenAI) to improve network security in software-defined networking. While software-defined networks provide a more programmable infrastructure, they are not immune to network security threats. Through a combination of Software-Defined Networking (SDN) technologies and generative AI, it is possible to facilitate improved SDN security approaches that promise enhanced network efficiency and protection. Among these approaches, generative adversarial networks (GAN) based models can be employed to generate adversarial traffic samples to train the proposed AI engines proven to be effective in detecting malicious network traffic. Additionally, generative artificial intelligence can …

The Impact Of Ai Use In Programming Courses On Critical Thinking Skills, Christian Jay St Francis Clarke, Abdullah Konak

Journal of Cybersecurity Education, Research and Practice

Proficiency in computer programming extends far beyond memorizing syntax; it depends on the cultivation of critical thinking. Computer programming requires multiple interconnected competencies, including systematic problem analysis, algorithmic reasoning, mastery of programming languages, debugging capabilities, a comprehensive understanding of software development methodologies, rigorous testing practices, and systematic troubleshooting approaches. These skills are also essential for cybersecurity experts; cybersecurity programs require several programming courses to enhance students’ technical and critical thinking skills. Generative AI (GenAI) technologies have fundamentally changed the process of developing applications and approaches to teaching coding. The growing use of GenAI technologies by students in writing computer code …

Optimizing Llm X86 Assembly Code Comprehension Through Fine-Tuning, Darrin Michael Lea

LSU Master's Theses

Reverse engineering is a cybersecurity process that focuses on understanding the underlying functionality of software or malware. This is an arduous process that demands large amounts of time and effort from cybersecurity practitioners. Large Language Models (LLMs) offer a potential solution to this problem. LLMs have worked their way into various fields of cybersecurity in recent years, including incident response and malware classification. However, LLMs have historically struggled with low-level code comprehension: a necessary part of reverse engineering. While LLMs can generate code and explain its function on the surface level, they struggle to grasp the wider context. In this …

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 …

Need Of Paradigm Shift In Cybersecurity Implementation For Small And Medium Enterprises (Smes), Shekhar Pawar, Hemant Palivela

International Journal of Cybersecurity Intelligence & Cybercrime

The increasing digitization of small and medium enterprises (SMEs) has significantly increased their attack surface, creating opportunities for various cyberthreats. In the global market, there are various cybersecurity standards and frameworks available, but there are still many cyber news stories from each corner of the world talking about increasing sophisticated cyber-attacks among organizations. According to recent studies, one out of five cyberattacks is targeting SMEs. Even though SMEs are relatively smaller as individuals, they are responsible for maximum contribution towards the betterment of the global economy, including the highest role in GDP and various employment opportunities. As compared to large …

A Survey On Unauthorized Uav Threats To Smart Farming, Peng Chen, Shihao Yan, Helge Janicke, Arash Mahboubi, Hang Thanh Bui, Hamed Aboutorab, Michael Bewong, Rafiqul Islam

Research outputs 2022 to 2026

The integration of Internet of Things (IoT) and unmanned aerial vehicles (UAVs) in smart farming has revolutionized agricultural practices by enhancing monitoring, automation, and decision-making to improve agricultural productivity and sustainability. However, the widespread use of these technologies has also introduced new security challenges, particularly the risk of interference from unauthorized UAVs. This survey provides an analysis of the threats posed by unauthorized UAVs to smart farms, highlighting potential vulnerabilities such as data interception, communication jamming, and physical damage. This paper first explores recent advancements in IoT and UAV technologies, which are integral to the functioning of smart farms. Then, …

The Urgency Of Instituting Systemic Cybersecurity Curriculum Within Stem At Secondary Educational Levels In Preparation For Postsecondary Institutions., Robert Spencer

Journal of Cybersecurity Education, Research and Practice

Over the last 20 years, many secondary institutions have made advances developing curriculum defined as “STEM (Science, Engineering and Mathematics)” in order to ensure secondary students are eligible to apply as well excel in technology degree programs at the college and university levels. Although various initiatives exist, there are studies however, which allude to a great possibility that there will be a lack of cybersecurity professionals filling present day and anticipated future positions. Despite a large number of federal and educational enhancements there is need for additional research regarding instituting overall systemic processes and curriculum which supports secondary student transition …

Ransomware In Healthcare: Threats, Impacts, And Mitigation Strategies, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

Excerpt: The growing digitalization of healthcare has exposed hospitals to significant cybersecurity threats, particularly ransomware attacks. The Health Sector Cybersecurity Coordination Center (HC3) reported that as of mid-2024, there were 730 cyber-attacks worldwide against healthcare institutions, with 530 targeting the U.S. (AHA, 2024). Half of these incidents involved ransomware, a type of malware that restricts access to critical data until a ransom is paid (HHS, 2021). Hospitals are attractive targets for cybercriminals due to their essential role in patient care. Cybercriminals exploit vulnerabilities in hospital networks, often causing severe operational and financial damage. Factors such as understaffed IT teams, outdated …

Cyber Threats In Healthcare: The Ransomware Epidemic, Mackenzie Dotson, Kasi Gorli, Alberto Coustasse

Management Faculty Research

In this presentation, we will delve into the growing ransomware crisis in healthcare, examining how these cyber threats disrupt hospital operations, jeopardize patient safety, and impose significant financial burdens. From understanding how ransomware infiltrates hospital systems to exploring real-world case studies, we will uncover the devastating impact of these attacks. Our discussion will also focus on mitigation strategies, cybersecurity best practices, and policy recommendations to safeguard healthcare institutions from future threats.