Cybersecurity Commons^™

From Network Packets To Physical Consequences: Network-Level Spoofing And Loss Of Visibility In Industrial Control Systems, Lillian B. Beck

LSU Master's Theses

Industrial Control Systems (ICS) are the foundation of critical infrastructure as they provide resources like clean water, electricity, and natural gas to support our everyday functions. Given their essential role in modern society, the failure or compromise of ICS systems can lead to significant negative consequences and public safety impacts. Understanding how these systems can be targeted and exploited by malicious actors is necessary to improve their security and resilience.

This research examines how an attacker could manipulate a real-world ICS network by targeting a fully functional Natural Gas (NG) Compressor Station Platform that emulates real operations. This research led …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

Data-Driven Prioritization Of Cybersecurity Vulnerabilities Using Business Context, Pierce Read, George Antoniou

Faculty and Staff Publications & Presentations

No abstract provided.

Privacy Preserving Anomaly Detection System For Der Solar Inverters, Tanzim Jim Hassan, Prakash Ranganathan

Graduate Research Achievement Day Posters

Reliable operation of solar inverters depends on maintaining a stable frequency. Recent cyber attacks on solar DERs are concerning and increase the likelihood of such stealthy attacks leading to anomalies in DER. Therefore, a robust anomaly detection system (ADS) is crucial for solar inverters used in distributed energy resources (DERs), enabling timely detection and correction of frequency anomalies. Additionally, preserving data privacy is essential for the security and reliability of the power grid. This paper proposes a privacy-preserving anomaly detection system (PP-ADS) based on a multi-stage hybrid machine learning (MSHML) model specifically designed for solar inverter data in DER environments. …

Evaluating The Effects Of Anti-Forensic Activities In Additive Manufacturing Devices, Daniel B. Miller

Shelby Hall Graduate Research Forum Posters

Additive Manufacturing (AM) is a newer famlily of production tecchologies that constructs objects by fusing layers of material into the desired shape. Methods for achieving this as described in Gibson et al. [1] are varied and include Fused Filament Deposition, Selective Laser Sintering, Stereolithography (SLA), and Powder Bed Fusion. Computers are integral to the processes being responsible for creating and decoding design instructions, collecting and processing sensor data, and, ultimately, directing the activity of the machines which implement the process. Additionally, the AM industry is rapidly expanding, worth an estimated $23 billion in 2023 and projected to reach $88 billion …

Cellebrite Reliability In Digital Forensics, Christina Huynh

Shelby Hall Graduate Research Forum Posters

Forensic tools like Cellebrite are commonly used in court to gather and interpret raw data for evidence. Cellebrite does not only collect data but creates and interprets the artifacts of data to create a scene of the process it has been through. With this, evidence can be influenced by software designs and not just the data on the mobile device. Courts and police use Cellebrite to gather evidence and reconstruct it to create an easily readable dataset. These tools lack reproducibility, transparency, integrity, and chain of evidence command. Cellebrite is often used in court and by police without further vetting …

Detecting Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton Purdy

Shelby Hall Graduate Research Forum Posters

From around the globe, malicious actors continually probe critical infrastructure assets for weaknesses. Their backgrounds, goals, and motives may vary, but the purpose of their attacks is the same: to damage, undermine, or exploit the functionality of these assets [2]. At a fundamental level, critical infrastructure is any essential system and asset vital to national security. Critical infrastructure includes assets such as power, transportation, telecommunications, water and wastewater systems (WWS), and many more [3].

Development Of An Artificial Immune System Based Intruder Detection Algorithm For Comparison With A Novel Intruder Detection Strategy., Jaden Caradine

Student Research Symposium (SRS)

"This project aims to develop an Artificial Immune System inspired intruder detection system based on current state-of-the-art strategies. Traditional intruder detection systems are not equipped to handle the evolving landscape of cyber security. Signature based detection systems require a database of known signatures and traditional anomaly detection systems are plagued with false positives. By contrast, Artificial Immune Systems can respond to threats never encountered. They are adaptive, proactive, and resilient to interference. This makes them ideal for determining when a system is behaving abnormally. Researchers are developing a novel intruder detection system, but they need a baseline to compare it …

Cybersecurity In Higher Education Institutions: Awareness, Policy, And Experience On Employee Behaviour, Abdullahi Abiodun Yusuf, Adriana A. Steyn

Journal of Cybersecurity Education, Research and Practice

The digital transformation of higher education institutions (HEIs) has introduced unprecedented connectivity and operational efficiency, but it has also heightened their exposure to cyber threats. South African HEIs, in particular, face increasing vulnerability due to their reliance on technology, openness, and diverse user communities. This study examines the influence of the institutional cybersecurity environment on employee cybersecurity-compliant behaviour (CCB), emphasising the critical roles of awareness, policy engagement, and experience. Drawing on Protection Motivation Theory and the Theory of Planned Behaviour, a conceptual model was developed to explore how cybersecurity awareness, policy familiarity, and prior experience shape employees’ attitudes, subjective norms, …

Future Trends In Cybersecurity: A Meta-Review, Yara Mohammed, Manar Alsaid, Gahangir Hossain

Faculty Publications

The increasing importance of cybersecurity in protecting digital assets, data and infrastructures necessitates a reevaluation of research priorities within the discipline. As of today, numerous emerging cybersecurity topics are gaining significant importance in both academic research and industry applications. To identify recent trends in cybersecurity topics, this study extracts scholarly articles from two prestigious academic databases, the ACM Digital Library, and Google Scholar, covering the period from early 2015 to late 2024.Through a systematic identification of trends and focal points in cybersecurity research, a comprehensive analysis is facilitated, including Latent Dirichlet Allocation (LDA), Biterm Topic Modeling (BTM), keyword frequencies, and …

Bridging The Gap: A Systematic Review Of Cyber Conflict Forecasting Models And The Case For Ai-Driven Dynamic Frameworks, Salim Arfaoui, Youssef Harrath, Omar El-Gayar

Research & Publications

Cyber conflict forecasting remains constrained by static models that overlook the integration of geopolitical context with technical indicators. This systematic literature review examines 58 studies (2010–2025) using PRISMA guidelines and an InputProcess-Output framework to classify approaches and identify key gaps. Quantitative methods dominate (67%), yet only 14% incorporate geopolitical variables, despite the political nature of cyber conflict. Major limitations include adversarial adaptation blindness (85% assume static behavior), coarse temporal granularity (72% use daily+ intervals), lack of uncertainty quantification (75%), and minimal modeling of cross-domain escalation (92% cyber-only focus). Strategic forecasting is rare, with just 14% providing long-term insights and 16% …

Digital Redlining In The Smart City: Artificial Intelligence, Housing Law, And Structural Urban Inequality, Spurthi Nrusimhadevara

Undergraduate Scholarship and Creative Works

Artificial intelligence is increasingly used in urban housing systems, where it shapes decisions about tenant screening, rent pricing, lending, zoning, and neighborhood investment. Although these tools are often promoted as efficient and impartial, they frequently rely on historical data that reflect racial, economic, and spatial inequality. As a result, AI systems can reproduce discriminatory outcomes even when protected characteristics are not directly used. This paper examines digital redlining in the smart city and argues that algorithmic housing tools mirror long standing structural inequities that raise significant concerns under fair housing and civil rights law. It evaluates how automated screening, predictive …

Big Tech As Transnational Spyware Regulator, Natalie R. Davidson

Fordham Intellectual Property, Media and Entertainment Law Journal

Spyware has emerged as a potent tool for leaders to shrink dem- ocratic contestation. In response to calls for constraints on the trade in spyware, states have updated the principal multilateral agree- ment on export controls, civil society groups have employed strate- gic litigation, and the European Union has altered its regulation, in each case with the aim of limiting exports where there is a risk of human rights violations. Yet, scandals involving the Israeli company NSO, among others, have made clear that even the updated regula- tory landscape is inadequate. Many actors are currently debating the reasons for existing …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …

Evaluating Llms For Cpe Identification In Iot Reconnaissance, Christopher Davisson

EWU Masters Thesis Collection

Vulnerability identification during penetration testing relies on rigid string-matching to map network scan data to Common Platform Enumeration (CPE) identifiers and downstream Common Vulnerabilities and Exposures (CVEs). The approach frequently fails on physical Internet of Things (IoT) devices, which produce non-standard, irregular service banners that resist deterministic parsing. Large Language Models can reason through these fuzzy associations, but cloud-hosted models introduce cost, latency, and operational security concerns when processing reconnaissance data from live networks. This thesis asks whether locally-hosted open-weight Large Language Models (LLMs) can perform this task well enough to be useful, and how performance varies with model scale, …

Systematic Approaches To Characterizing Vulnerabilities And Enhancing Robustness Of Text And Vision-Language Models, Poojitha Thota

Computer Science and Engineering Dissertations

The proliferation of artificial intelligence (AI) across critical domains, including news summarization, privacy-policy analysis, and medical decision support, has raised growing concerns about the security and robustness of these systems against adversarial manipulation. This dissertation investigates adversarial robustness in generative AI by addressing three key research goals: (1) characterizing adversarial vulnerabilities across generative models, (2) developing systematic defenses to improve the robustness of generative models, and (3) designing deployment-time safeguards for securing LLM interactions.

Towards the first goal, we characterize adversarial vulnerabilities across text-based and multimodal systems. In abstractive text summarization, we show that inference-time perturbations can exploit lead bias …

Ordered Mini-Batch Training For Differentially Private And Encrypted Logistic Regression, Ryan Leone

Theses, Dissertations and Culminating Projects

Logistic regression has found extensive use as a supervised machine learning algorithm due to its simplicity and efficiency in binary and multivariate classification tasks. As data sharing grows across connected devices, safeguarding sensitive personal and industrial information is of increased importance. Privacy-preserving machine learning techniques such as differential privacy and homomorphic encryption offer mathematically rigorous security guarantees, but introduce difficult accuracy, privacy loss, and computational overhead issues. This thesis investigates PPML for logistic regression through a collaborative mini-batch training framework. I propose and implement an ordered mini-batch strategy, compare it to standard shuffled methods, then integrate differential privacy noise injection …

Understanding Phishing Susceptibility Through Expert Consensus Using Digital Marketing Parallels And A Machine Learning-Based Implementation, Mansoor Ahmad

All Graduate Theses, Dissertations, and Other Capstone Projects

Phishing remains one of the most effective attack vectors for gaining unauthorized access to organizational systems, yet defenders often lack systematic methods to assess their exposure before an attack. This study develops a framework that uses machine learning to encode the collective expertise of cybersecurity practitioners into a portable phishing susceptibility assessment tool, with the goal to help security teams proactively identify patterns, prioritize awareness training, and strengthen detection controls. The study surveyed 27 practitioners with extensive experience in social engineering, red teaming, penetration testing, and threat analysis to identify which factors most influence phishing susceptibility. Practitioners provided quantitative ratings …

Characterizing Cyber Intrusions In Critical Infrastructure Networks Using Discrete-Event Simulation, Lawrence M. Dilworth

Dissertations, Master's Theses and Master's Reports

Over the past two decades, cybersecurity compliance frameworks such as the North American Electric Reliability Corporation Critical Infrastructure Protection (CIP) have introduced prescriptive measures for protecting power system networks, emphasizing restricted access, segmentation, and minimizing routable exposure. While effective for baseline cyber hygiene, these approaches do not capture system-level risks or adversarial propagation across interconnected infrastructure. In contrast, Cyber-Informed Engineering (CIE), advanced by Idaho National Laboratory, embeds security in system design by considering threat vectors and physical constraints.

Despite CIP guidance, many deployments rely on IP-routable, bidirectional communication that enables handshaking, allowing adversaries to infer reachable targets. This work presents …

Towards Breach Hypothesis Based Predictive Autonomous Cyber Defense Ecosystem: Proactive Prevention Of Imminent Threats And Productivity Losses, Yogesh Chavarkar

Master's Theses and Doctoral Dissertations

As cyberattack tools and techniques get sophisticated and persistent, reactive cybersecurity has been unable to effectively prevent breaches and compromises. Organizations and institutions with large complex environments have wide vulnerable exposure with higher chances of a cyberattack. This also increases overall security and financial risk. Possibility of repetitive attacks from cyber threats increases, too, despite the use of standard defense measures. Security tools and system vulnerabilities often need manual patching and updates to keep the environment secure and functioning effectively. Productivity suffers from manual trade-offs in keeping the systems secure from adverse impact. Persistent high-severity cyberattacks, despite continued reactive defensive …