Digital Commons Network^™

Directing Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton C. Purdy, Jordan Shropshire

Shelby Hall Graduate Research Forum Posters

In recent years there has been an increasing number of cyberattacks on public water generation and distribution systems. Advanced persistent attackers could usurp sensor and control systems to contaminate public drinking water. In order to conceal their malicious activity, they can manipulate sensor data flows to give the appearance of normal activity. The compromised sensors would report normal chemical levels even though unsafe water is entering the distribution system. In response, this research proposes a multi-sensor, cross-validation approach to anomaly detection. The proposed approach is designed to detect sophisticated cyberattacks which are not easily detectable using traditional cyber tools. The …

Generative Ai And Llms For Critical Infrastructure Protection: Evaluation Benchmarks, Agentic Ai, Challenges, And Opportunities, Yagmur Yigit, Mohamed Amine Ferrag, Mohamed C. Ghanem, Iqbal H. Sarker, Leandros A. Maglaras, Christos Chrysoulas, Naghmeh Moradpoor, Norbert Tihanyi, Helge Janicke

Research outputs 2022 to 2026

Critical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, …

Managing Cybersecurity In Local Governments: 2022, Donald F. Norris Phd, Laura K. Mateczun Jd

Journal of Cybersecurity Education, Research and Practice

This paper, based on data from our second nationwide survey of cybersecurity among local or grassroots governments in the U.S., examines how these governments manage this important function. As we have shown elsewhere, cybersecurity among local governments is increasingly important because these governments are under constant or nearly constant cyberattack. Due to the frequency of cyberattacks, as well as the probability that at least some attacks will succeed and cause damage to local government information systems, these governments have great responsibility to protect their information assets. This, in turn, requires these governments to manage cybersecurity effectively, something our data show …

Locational Data And The Public Interest, William A. Herbert, Micahel Goodchild, Richard Appelbaum, Jeremy Crampton, Gary Langham, Krzysztof Janowicz, Mei-Po Kwan, Katina Michael, Lisa Schamess

Publications and Research

This article presents a paper developed by the AAG Organizing Committee on Locational Information and the Public Interest through a summit held in Santa Barbara, California in June 2022. The summit resulted in goals and ideas for addressing the issues that arise from the present environment for geodata, whereby public, private, and third-sector entities can tap into publicly available locational information with relatively little regulation on its access or use. The Committee articulates four goals: (1) develop a research agenda extending across disciplines, (2) outline educational resources and strategies to guide ethical practice, (3) devise a pathway to increase public …

A Content-Based Recommender System For The Uav Caching In The Field Of Entertainment In Fog Computing, Elham Darbanian, Mohsen Nickray

Turkish Journal of Electrical Engineering and Computer Sciences

The Unmanned Aerial Vehicle (UAV) can be used as good flying base stations to cache popular content and follow a user mobility pattern, to help them in a suitable services. Conventional edge caching algorithms often prioritize cache contents with higher popularity. Nevertheless, the cache capacity of mobile devices is restricted, and diverse clients may have expansive varieties in content inclination designs. In this manner, the performance and effectiveness of the cache will be so constrained without great strategies. The composition of recommender system and edge caching is considered as a new research topic, which is used to reduce cost and …

Mosaicos De La Comunidad (Mosaics Of The Community): Community-Engaged Participatory Muraling With Madres Emprendedoras, Jesica S. Fernandez, Laura A. Nichols

Ethnic Studies

In this paper, we describe a collaborative community-based research project that centered on community members’ lived experiences, which led to the identification of key community issues that resulted in a representative art project in the form of a public mural. Eleven mothers who were long-time residents of the community were the drivers of the issue identification and mural creation. The issues identified, and subsequently depicted in the mural, revealed the importance of the environment in neighborhoods, with residents dealing with encampments, illegal dumping, prostitution, eviction, and gentrification. In the mural Mosaicos de la Comunidad (Mosaics of the Community), a group …

Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times for charging, and difficulty in selecting appropriate CPs for EVs. To address these challenges, we propose a novel end-to-end framework, called Stable Matching based EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with the assistance of roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a …

“Science Belongs Everywhere”: Outlooks On Post-Secondary Stem Education For Correctional Centers In Virginia, Shelita R. Augustus

Division of Strategic Enrollment Management and Student Success Publications

Virginia has an incarceration rate of 679 per 100,000 residents, the highest among independent democratic nations. This scoping review examines perspectives on post-secondary STEM education to promote STEM education within Virginia's correctional facilities, assesses programs to improve rehabilitation, reduce recidivism, and address systemic inequalities by developing the scientific potential of incarcerated individuals. The review finds that while STEM education in correctional facilities faces limitations, its potential benefits are being realized nationwide. Virginia has made strides through initiatives like House Bill 2158, which aims to expand higher education access for incarcerated individuals. Examples such as the programs at J. Sargeant Reynolds …

Analyzing Patterns In Chicago Motor Vehicle Crashes Using Time-Series Techniques, Christina Trotta

Senior Honors Theses and Projects

This project explores time series forecasting of daily traffic crash rates in Chicago from 2018 to 2024, with a focus on understanding how past crash patterns and external conditions influence future risk. The primary research question asks: To what extent does yesterday’s crash rate help predict today’s? Using a combination of Holt-Winters exponential smoothing, Prophet forecasting, and SARIMAX models, we assess the role of autoregression, seasonality, and exogenous variables such as weather and roadway conditions. Daily crash data was cleaned, aggregated, and enriched with engineered features including holiday indicators, weather metrics from O’Hare and Midway airports, and binary flags for …

Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz

Master's Theses and Doctoral Dissertations

The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …

Landslide Susceptibility Mapping Via Dempster-Shafer, Statistical Index, And Certainty Factor Models In Gis And Their Comparison At Pidie Recency In Aceh, Indonesia, Septianto Aldiansyah, Amniar Ati, Fitriyani Saudi

Applied Environmental Research

Landslides are natural disasters that are active if there is an interaction of environmental factors that are considered to control them, especially in mountainous areas. This study developed a landslide susceptibility map in Pidie Regency via the Dempster-Shafer (DS), statistical index (SI), and certainty factor (CF) models. A total of 957 landslide events were mapped, 70% of which were used for modeling, whereas the remaining events were used to validate the model output. Fourteen layers of conditioning factors were used: elevation, slope, aspect, curvature, TWI, SPI, STI, NDVI, rainfall, distance from river, distance from road, distance from fault, LULC, and …

Into Thick Air: Adapting Cultural Practices Into Solutions For Modern Wicked Problems, Syed Muhammad Erzum Naqvi

Theses and Dissertations

Into Thick Air argues that cultural practices can inspire solutions

to challenging modern problems. This project employs the Basant

festival as a case study to critically reimagine kite flying as a

collective practice with the potential to contribute to the reduction

of air pollution in Lahore, a city consistently ranked among those

with the poorest air quality globally. By building kites from fabric

treated with nanoparticle coatings, Into Thick Air transforms a

traditional community activity into an accessible, citizen-driven

method for removing pollutants from the air. The treated kites

empower Lahoris to take charge of their environment while

encouraging collective …

Hydrogen Readiness In Aviation & Challenges- Technology Meets Regulation And Market Demand, Eva Maleviti

Publications

Where technology meets regulation and market demand.

Forecasting Hourly Police Call For Service Volumes: A Comparative Analysis Of Statistical, Machine Learning And Neural Network Models For Operational Planning., Patrick Duggan

ICT

Accurate demand forecasting is critical in operational settings where resource allocation and planning decisions depend on anticipated service volumes. Transactional systems that capture timestamped records provide valuable data sources for developing demand forecasts. This study examines hourly call volume forecasting using New Orleans police calls for service data, comparing the performance of statistical models, tree-based methods, and recurrent neural networks.

The research evaluates four primary modelling approaches: ARIMA models representing traditional statistical methods, XGBoost and Random Forest as a tree-based ensemble technique, and Gated Recurrent Units (GRUs) as deep learning alternatives. A naive seasonal model serves as the baseline benchmark. …

Comparative Study Of Environmental Impacts Of End-Of-Life Management Approaches For Air Conditioners Using Lca., Viveha Sriskandaraja

Chulalongkorn University Theses and Dissertations (Chula ETD)

The amount of e-waste generated worldwide has surpassed 62 million tons, which has created consequences related to the management and disposal of e-waste. The primary concern regarding air-conditioners is waste management, as these units consist of plastics, metals, hazardous materials, and refrigerants. Improper disposal or leakage of refrigerants poses a significant greenhouse gas threat that accelerates climate change. The end-of-life (EOL) stage is often overlooked in the management of discarded air-conditioners. Therefore, this study focuses on filling the knowledge gap regarding the comparison of EOL management methods and analysing the effectiveness of regulated management methods. This study aims to use …

Fortifying Nuclear Security: Cultivating Vigilance Culture Among Personnel For Enhanced Prevention And Response, Sabariah Kader Ibrahim, Sonia Naz

International Journal of Nuclear Security

A vigorous nuclear security culture is crucial in preventing the theft and sabotage of nuclear material. It requires vigilance from all employees because relying solely on closed-circuit television systems and security personnel is insufficient to deter potential threats. Employee attentiveness plays a vital role in recognizing and preventing suspicious activities. However, achieving a strong security culture is challenging because of the lack of incidents in some facilities, leading to employee complacency.

This paper highlights the pivotal role of personnel in the success of nuclear security measures, emphasizing the need for a proactive and vigilant mindset among staff. Through the analysis …

Improving Understanding And Connections For Preparedness, Mitigation, Response, And Recovery For Tornado And Straight-Line Wind Events In Virginia: Summary Of The Inaugural Virginia Tornado And Straight-Line Wind Summit, Wie Yusuf, Monica Arul, Joshua Behr, Kaleen Lawsure, An Huy Pham, Henry Attivor

School of Public Service Faculty Publications

Background

The Virginia Tornado and Straight-Line Wind Summit, the first of its kind in the Commonwealth, was held on October 15, 2025 in Isle of Wight County, Virginia. The Summit had several goals, including:

• Understanding Virginia’s wind risk and learning the science behind it.
• Hearing lessons learned from emergency managers who have experienced extreme wind events.
• Learning about practical steps to prepare, mitigate, and respond to severe wind events.
• Learning best practices for timely and actionable communication with the public before and after events.
• Strengthening interagency coordination and connecting with others to share resources, align priorities, and coordinate resilience planning. …

Forecasting Hourly Police Call For Service Volumes: A Comparative Analysis Of Statistical, Machine Learning And Neural Network Models For Operational Planning., Patrick Duggan

ICT

This study investigates hourly call volume forecasting for New Orleans police service data, comparing statistical, tree-based, and deep learning approaches. ARIMA models represent traditional methods, XGBoost and Random Forest serve as tree-based ensembles, and Gated Recurrent Units (GRUs) provide deep learning alternatives, with a naive seasonal model as a baseline. Models are evaluated using a practical, expanding time-block framework simulating real operational deployment. Results show GRUs achieve the highest accuracy (R² = 0.74, MAPE = 13.5%), with XGBoost performing similarly, while ARIMA underperforms. Additionally, a Random Forest model offers interpretability, identifying key factors that drive forecasting performance, providing actionable insights …

Sparse Transformer For Anomaly Detection In Mobile Crowdsensing, Sanjeev Shrestha

Graduate Theses/Dissertations

Mobile Crowdsensing (MCS) is a sensing paradigm that leverages mobile devices to conduct a large-scale data collection. However, due to its openness and mobility nature, it is highly vulnerable to security issues such as injection attacks of malicious workers and fake tasks that can severely affect the platform’s normal functioning. To address this problem, the arrival of workers and task submission process is represented as a multivariate time series, and a two-stage framework is proposed. In the first step, we propose a novel transformer-based model, DozerAnomaly, that can efficiently detect anomalies in multivariate time series. We integrated a sparse attention …

Modeling Recreational Risk Factors On The Landscape: A Predictive Gis Analysis Of Recreation Accident Response Near North Bend, Washington., Naomi S.J. Van Roon

All Master's Theses

Search and Rescue (SAR) teams in Washington respond to approximately 900 incidents annually, many stemming from outdoor recreation near Interstate 90 surrounding North Bend. This study utilized predictive GIS analysis to identify areas of concern within state lands, including Mount Si and Middle Fork Snoqualmie Natural Resource Conservation Areas, Raging River State Forest, and Rattlesnake Mountain Scenic Area. The predictive model was calibrated using SAR responses to hiking-related incidents between March and November from 2020 to 2023, and incorporated an analysis of risk factors influencing recreationalist incident probability. This research then developed a decision support system to aid land managers …

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

Integrating Spatial Statistics And Decision Analysis For Wildfire Risk Mapping: A Case Study Of The Kootenai National Forest, Elijah Kordieh Mensah

Graduate Student Theses, Dissertations, & Professional Papers

Wildfire risk in the western United States has intensified in recent decades due to intersecting forces of climate change, systematic fire suppression, and expanding human settlement in fire-prone regions. This thesis presents a comprehensive spatial assessment of wildfire risk in the Kootenai National Forest (KNF) and its surrounding landscape in northwestern Montana, integrating wildfire likelihood, suppression difficulty, evacuation vulnerability, and building exposure into a unified spatial statistical framework. The research addresses a critical gap in spatial wildfire risk modeling by focusing on the need to assess fire threats in relation to local operational constraints and community vulnerabilities.
Drawing on geospatial …

Analyzing Car Theft Trends In Central Texas: A Comparative Study Of Waco, College Station, And Killeen, Daniel Njogu

Williams Honors College, Honors Research Projects

This study examines motor vehicle theft (MVT) trends from 2019 to 2023 in three Central Texas cities—Waco, College Station, and Killeen—using temporal analysis, geospatial hotspot mapping, and make/model data. In Killeen, thefts generally rose over the period, with notable peaks in October and on Mondays. College Station saw an overall decline in thefts but experienced a seasonal spike each March, and Waco’s thefts increased until around 2021 before beginning to fall. Local festivals—such as the Spirit of Texas in College Station and the Heart O’ Texas Fair in Waco—appear to coincide with these seasonal upticks. Hyundais and Kias were most …

On The Validity Of Traditional Vulnerability Scoring Systems For Adversarial Attacks Against Llms, Atmane Ayoub Mansour Bahar, Ahmad Samer Wazan

All Works

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks on Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of different metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. Approach - This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by …

Towards Achieving The Un Sustainable Development Goals: The Role Of Ai In Municipality Services, Thabit Sultan Mohammed, Karim Mohammed Aljebory, Ahmed Thabit Sultan

Mesopotamian Journal of Computer Science

In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) to end poverty, protect the planet, and ensure global peace and prosperity by 2030. However, progress has been hindered by challenges like the COVID-19 pandemic, climate change, funding shortages, political instability, and data limitations. Municipal services, crucial to achieving the SDGs, provide essential functions like waste management, healthcare, and public safety. Artificial Intelligence (AI) offers innovative solutions to enhance these services, improving urban sustainability and fostering public-private collaboration. This research examines AI's role in municipal services, analyzing its applications, benefits, challenges, and future potential through case studies and expert …

Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur

Theses and Dissertations (Comprehensive)

It is often assumed that natural phenomena occur randomly over time. However, careful analysis reveals that these events typically form some series or sequences and exhibit distinctive temporal patterns. These patterns are not exclusive to nature. They also appear in human activities, often studied under the concept of bursty human dynamics. The statistical methods analyzing bursty human dynamics not only capture overall trends or seasonality but also explore how past events influence future ones. It makes the analysis more realistic and the results more closely aligned with reality. Bursty human dynamics can be studied at two levels: the individual level …

Bioaccumulation Of Legacy And Novel Pfas In The Environment, Hesham Taher, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

The bioaccumulation of per- and polyfluoroalkyl substances (PFAS), both legacy and novel, in the environment presents significant ecological and health risks. PFAS are a diverse group of synthetic chemicals known for their persistence and bioaccumulation, which can cause widespread environmental contamination and health risks. Strong carbon-fluorine bonds give these compounds unparalleled stability, preventing them from degrading and enabling them to endure in a range of environmental matrices, including water, soil, and biota. Legacy PFAS, including perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), have been extensively studied and regulated, resulting in lower concentrations in some environmental media. However, novel PFAS, …

Snake Optimization Algorithm For Fractional Order Controller In Three-Area Load Frequency Regulation, Shubra Goel, Omveer Singh

ASEAN Journal on Science and Technology for Development

The transition toward renewable energy-dominated power systems have accentuated the intricacies of frequency control, necessitating advanced regulatory mechanisms. This investigation articulates a tri-zonal frequency stabilization approach, employing a hybridized controller that synergizes Fuzzy Fractional-Order PI and Tilt-Integral-Derivative methodologies. The uniqueness of this approach is further amplified by the deployment of the Snake Optimization algorithm for precise parameter tuning. Set within a conventional grid topology integrated with assorted renewable energy sources, the study evaluates the controller’s adaptability and resilience through a series of comprehensive scenario-based analyses.

Laboratory Investigation Of Shape And Initial Orientation Effects On Surf Zone Object Migration, Temitope E. Idowu, Jack A. Puleo

Civil & Environmental Engineering Faculty Publications

Discarded objects like munitions in marine environments pose public safety risks. The behavior of various density objects deployed at four cross-shore positions in the surf zone of a large-scale 120 m x 5 m x 5 m wave flume were observed under different forcing conditions. Net migration was predominantly directed offshore, with approximately 70 % offshore migration observed near the outer surf zone. Density, shape, and initial orientation were identified as important to object behavior, with density acting as the dominant driver in 67 % of the object pairing scenarios. The influence of shape and initial orientation on net migration …

Leveraging Artificial Intelligent For Optimized Crop Production: An Ann-Based Approach, Yahya Layth Khaleel, Fadya A. Habeeb, Mustafa Abdulfattah Habeeb, Fatimah N. Ameen

Mesopotamian Journal of Computer Science

To incite modern day crop production and ensure sustainability, exact crop recommendations are key to the process. This study pays significant attention to the need for the use of big data tools in studies involving comprehensive data sets that contain information on soil and other environmental characteristics. The set of data used in this research includes Nitrogen, Phosphorus, and Potassium content coordinated with Temperature, Humidity, pH Value, and Rainfall. Knowing these factors is to make a favorable decision about improving agricultural products yield, availability and management of the resources, as well as general well-being of the crops. Specialized advisory on …