Engineering Commons^™

A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka

Cybersecurity Undergraduate Research Showcase

The internet needs secure forms of identity authentication to function properly, but identity authentication is not a core part of the internet’s architecture. Instead, approaches to identity verification vary, often using centralized stores of identity information that are targets of cyber attacks. Decentralized identity is a secure way to manage identity online that puts users’ identities in their own hands and that has the potential to become a core part of cybersecurity. However, decentralized identity technology is new and continually evolving, which makes implementing this technology in an organizational setting challenging. This paper suggests that, in the future, decentralized identity …

Predictive Ai Applications For Sar Cases In The Us Coast Guard, Joshua Nelson

Cybersecurity Undergraduate Research Showcase

This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications …

A Case Study Of The Crashoverride Malware, Its Effects And Possible Countermeasures, Samuel Rector

Cybersecurity Undergraduate Research Showcase

CRASHOVERRIDE is a modular malware tailor-made for electric grid Industrial Control System (ICS) equipment and was deployed by a group named ELECTRUM in a Ukrainian substation. The malware would launch a protocol exploit to flip breakers and would then wipe the system of ICS files. Finally, it would execute a Denial Of Service (DOS) attack on protective relays. In effect, months of damage and thousands out of power. However, due to oversights the malware only caused a brief power outage. Though the implications of the malware are cause for researching and implementing countermeasures against others to come. The CISA recommends …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins

Cybersecurity Undergraduate Research Showcase

This paper will present the capabilities and security concerns of public AI, also called generative AI, and look at the societal and sociological effects of implementing regulations of this technology.

Ransomware: What Is Ransomware, And How To Prevent It, Brandon Chambers

Cybersecurity Undergraduate Research Showcase

This research paper answers the question, “What is Ransomware, and How to prevent it?”. This paper will discuss what ransomware is, its history about ransomware, how ransomware attacks Windows systems, how to prevent ransomware, how to handle ransomware once it is already on the network, ideas for training professionals to avoid ransomware, and how anti-virus helps defend against ransomware. Many different articles, case studies, and professional blogs will be used to complete the research on this topic.

Simulating Function Generators And Oscilloscopes In A Virtual Laboratory Environment, Yiyang Li, Yuzhong Shen, Charles I. Sukenik

Modeling, Simulation and Visualization Student Capstone Conference

This paper discusses the development of a virtual laboratory for simulating electronic instruments commonly used in science and engineering courses, such as function generators and digital storage oscilloscopes. Mathematical equations are used to represent continuous signals and ensure signal integrity, while C# delegates are adopted to enable communication between simulated devices. The approach allows for loose coupling between software components and high cohesion of individual components, and can be applied to other virtual laboratory developments. The virtual laboratory provides a means for students to gain hands-on experience with electronic instruments and improve their understanding of theoretical concepts.

Automatic Generation Of Virtual Work Guide For Complex Procedures: A Case, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Practical work guides for complex procedures are significant and highly affect the efficiency and accuracy of on-site users. This paper presents a technique to generate virtual work guides automatically for complex procedures. Firstly, the procedure information is extracted from the electronic manual in PDF format. And then, the extracted procedure steps are mapped to the virtual model parts in preparation for animation between adjacent steps. Next, smooth animations of the procedure are generated based on a 3D natural cubic spline curve to improve the spatial ability of the work guide. In addition, each step's annotation is automatically adjusted to improve …

Hampton Roads' Building Resilient Communities Flood Game, Gul Ayaz, Katherine Smith, Rafael Diaz, Joshua G. Behr

Modeling, Simulation and Visualization Student Capstone Conference

As rising sea levels and subsequent recurrent flooding disproportionately affects coastal areas, it is crucial to develop a heightened awareness of the impacts of natural disasters on communities and the environments they live in. The Hampton Roads’ Building Resilient Communities (BRC) Flood Game is a simulation role-playing game designed to allow players to increase their understanding of the impact of various community response interventions to sea level rise and recurrent flooding. Players will examine and assess the tradeoffs of resiliency investments, the impact policies may have on the population, and the amount of time return on investment takes. The BRC …

The Legacy Of Colonization And Civil Societies In South Africa, Erika Frydenlund, Melissa Miller-Felton, Bolu Ayankojo

Modeling, Simulation and Visualization Student Capstone Conference

This research analyzes the unique ways that civil societies operate in Sub-Saharan Africa in the context of post-apartheid Cape Town, South Africa. Decades after the demise of apartheid, remnants of inequality remain without the promise of actionable change. We used a computational modeling approach to understand the dynamics of migrants in the receiving community as derived from qualitative interviews conducted with 24 stakeholders in Cape Town, South Africa between 2020 and 2021. Our findings show that the presence of NGOs can promote access to resources and reduce xenophobia if they can have the right influence on government policies.

Digital Game-Based Approach To Math Learning For Students, Gul Ayaz, Katherine Smith

Modeling, Simulation and Visualization Student Capstone Conference

Mathematics is an important subject that is pervasive across many disciplines. It is also a subject that has proven to be challenging to both teach and learn. Students face many challenges with learning math such as a lack of motivation and anxiety. To address these challenges, game-based learning has become a popular approach to stimulate students and create a more positive classroom environment. It can serve as an alternative or supplement to traditional teaching and can better engage students while developing a positive attitude toward learning. The use of games in a classroom can create a more exciting and engaging …

Gpu Utilization: Predictive Sarimax Time Series Analysis, Dorothy Dorie Parry

Modeling, Simulation and Visualization Student Capstone Conference

This work explores collecting performance metrics and leveraging the output for prediction on a memory-intensive parallel image classification algorithm - Inception v3 (or "Inception3"). Experimental results were collected by nvidia-smi on a computational node DGX-1, equipped with eight Tesla V100 Graphic Processing Units (GPUs). Time series analysis was performed on the GPU utilization data taken, for multiple runs, of Inception3’s image classification algorithm (see Figure 1). The time series model applied was Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX).

Promoting Skills In Children And Teens With Autism Spectrum Disorder Through Play And Steam, Meaghan Mcleod Mozingo, Krzysztof J. Rechowicz

Modeling, Simulation and Visualization Student Capstone Conference

Individuals with autism spectrum disorder (ASD) have a low employment rate. This is caused by a lack of support from employment resources and the negative stigma associated with common characteristics associated with ASD. With limited career-building activities and events, it is difficult for individuals with ASD to identify their skills, strengths, and career opportunities. Parental support is crucial when seeking employment opportunities for their children. Through the use of play, children with ASD engaged and explored their skills with science, technology, engineering, arts, and math-centered activities. This paper highlights the events and shows the proposed redesign for an additional workshop.

Mask-On Breathing Awareness Trainer (Mobat), Robert Larson, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

Currently there is no low-cost solution to teach large quantities of Naval Aviators on how to correctly breath on an oxygen mask. This report will discuss the design and implementation of a mask-on breathing apparatus for use with United States Naval Aircrew for initial and annual familiarization. With the current uptick in physiological episodes within the naval aviation enterprise, there has been a need to find solutions to this problem. Currently when a naval aircrew is trained in aviation physiological threats, they do so with normobaric hypoxia tools in the form of the 9A19 Normobaric Hypoxia Trainer for aircrew who …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Assessing Frustration Towards Venezuelan Migrants In Columbia: Path Analysis On Newspaper Coded Data, Brian Llinás, Guljannat Huseynli, Erika Frydenlund, Katherine Palacia, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This study analyzes the impact of Venezuelan migrants on local frustration levels in Colombia. The study found a relationship between the influx of Venezuelan migrants and the level of frustration among locals towards migrants, infrastructure, government, and geopolitics. Additionally, we identified that frustration types have an impact on other frustrations. The study used articles from a national newspaper in Colombia from 2015 to 2020. News articles were coded during a previous study qualitatively and categorized into frustration types. The code frequencies were then used as variables in this study. We used path modeling to statistically study the relationship between dependent …

Agenda: Msvscc 2023, Jessica Johnson

Modeling, Simulation and Visualization Student Capstone Conference

Agenda for the 16th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference held on April 20, 2023 held in-person at the Virginia Modeling, Analysis and Simulation Center (ODU-VMASC).

The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley

Modeling, Simulation and Visualization Student Capstone Conference

Although visualization is beneficial for evaluating and communicating data, the efficiency of various visualization approaches for different data types is not always evident. This research aims to address this issue by investigating the usefulness of several visualization techniques for various data kinds, including continuous, categorical, and time-series data. The qualitative appraisal of each technique's strengths, weaknesses, and interpretation of the dataset is investigated. The research questions include: which visualization approaches perform best for different data types, and what factors impact their usefulness? The absence of clear directions for both researchers and practitioners on how to identify the most effective visualization …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

Behind Derogatory Migrants' Terms For Venezuelan Migrants: Xenophobia And Sexism Identification With Twitter Data And Nlp, Joseph Martínez, Melissa Miller-Felton, Jose Padilla, Erika Frydenlund

Modeling, Simulation and Visualization Student Capstone Conference

The sudden arrival of many migrants can present new challenges for host communities and create negative attitudes that reflect that tension. In the case of Colombia, with the influx of over 2.5 million Venezuelan migrants, such tensions arose. Our research objective is to investigate how those sentiments arise in social media. We focused on monitoring derogatory terms for Venezuelans, specifically veneco and veneca. Using a dataset of 5.7 million tweets from Colombian users between 2015 and 2021, we determined the proportion of tweets containing those terms. We observed a high prevalence of xenophobic and defamatory language correlated with the …

A Comparison Of Nonverbal And Paraverbal Behaviors In Simulated And Virtual Patient Encounters, Sarah Powers, Mark W. Scerbo, Matthew Pacailler, Macy Kisiel, Baillie Hirst, Ginger S. Watson, Lauren Hamel, Fred Kron

Modeling, Simulation and Visualization Student Capstone Conference

The present study assessed whether trainees display similar nonverbal and paraverbal behaviors when interacting with a simulated (SP) and virtual patient (VP). Sixty second slices of time following four interactions were rated for the presence and frequency of three nonverbal and paraverbal behaviors. Results revealed that students exhibited fewer behaviors in the VP interaction, possibly due to differences social inhibition or fidelity between the two formats.

An Algorithm For Finding Data Dependencies In An Event Graph, Erik J. Jensen

Modeling, Simulation and Visualization Student Capstone Conference

This work presents an algorithm for finding data dependencies in a discrete-event simulation system, from the event graph of the system. The algorithm can be used within a parallel discrete-event simulation. Also presented is an experimental system and event graph, which is used for testing the algorithm. Results indicate that the algorithm can provide information about which vertices in the experimental event graph can affect other vertices, and the minimum amount of time in which this interference can occur.

U-Net Based Multiclass Semantic Segmentation For Natural Disaster Based Satellite Imagery, Nishat Ara Nipa

Modeling, Simulation and Visualization Student Capstone Conference

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. Semantic segmentation is believed to be on of the best techniques to capture pixelwise information in computer vision. In this work we will be using a U-Net architecture to do a three class semantic segmentation for the Xview2 dataset to capture the level of damage caused by different natural disaster which is beyond the visual scope of human eyes.

Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.

Towards Nlp-Based Conceptual Modeling Frameworks, David Shuttleworth, Jose Padilla

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents preliminary research using Natural Language Processing (NLP) to support the development of conceptual modeling frameworks. NLP-based frameworks are intended to lower the barrier of entry for non-modelers to develop models and to facilitate communication across disciplines considering simulations in research efforts. NLP drives conceptual modeling in two ways. Firstly, it attempts to automate the generation of conceptual models and simulation specifications, derived from non-modelers’ narratives, while standardizing the conceptual modeling process and outcome. Secondly, as the process is automated, it is simpler to replicate and be followed by modelers and non-modelers. This allows for using a common …

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

Modeling, Simulation and Visualization Student Capstone Conference

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …

Lack Of Black Female Diversity Within The Cybersecurity Workforce, Eric Preston

Cybersecurity Undergraduate Research Showcase

Cybersecurity methods, strategies, and programs for protecting computers and electronic devices have become critically important in all aspects of the technological infrastructure. However, while this is a vastly growing field that has a large workforce, it suffers from a lack of representation, specifically for Black/African American females. This study examines the barriers that prevent Black women from having representation within the cybersecurity workforce and solutions that address them. Some barriers noted include a lack of role models and resources, historical discrimination and systemic barriers, and cultural norms. Additionally, implications created by these barriers include less diversity, a lack of career …

Role Of Ai In Threat Detection And Zero-Day Attacks, Kelly Morgan

Cybersecurity Undergraduate Research Showcase

Cybercrime and attack methods have been steadily increasing since the 2019 pandemic. In the years following 2019, the number of victims and attacks per hour rapidly increased as businesses and organizations transitioned to digital environments for business continuity amidst lockdowns. In most scenarios cybercriminals continued to use conventional attack methods and known vulnerabilities that would cause minimal damage to an organization with a robust cyber security posture. However, zero-day exploits have skyrocketed across all industries with an increasingly growing technological landscape encompassing internet of things (IoT), cloud hosting, and more advanced mobile technologies. Reports by Mandiant Threat Intelligence (2022) concluded …