Computer Sciences 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 …

The Vulnerabilities Of Artificial Intelligence Models And Potential Defenses, Felix Iov

Cybersecurity Undergraduate Research Showcase

The rapid integration of artificial intelligence (AI) into various commercial products has raised concerns about the security risks posed by adversarial attacks. These attacks manipulate input data to disrupt the functioning of AI models, potentially leading to severe consequences such as self-driving car crashes, financial losses, or data breaches. We will explore neural networks, their weaknesses, and potential defenses. We will discuss adversarial attacks including data poisoning, backdoor attacks, evasion attacks, and prompt injection. Then, we will explore defense strategies such as data protection, input sanitization, and adversarial training. By understanding how adversarial attacks work and the defenses against them, …

What Students Have To Say On Data Privacy For Educational Technology, Stephanie Choi

Cybersecurity Undergraduate Research Showcase

The literature on data privacy in terms of educational technology is a growing area of study. The perspective of educators has been captured extensively. However, the literature on students’ perspectives is missing, which is what we explore in this paper. We use a pragmatic qualitative approach with an experiential lens to capture students’ attitudes towards data privacy in terms of educational technology. We identified preliminary, common themes that appeared in the survey responses. The paper concludes by calling for more research on how students perceive data privacy in terms of educational technology.

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 …

Investigating Vulnerabilities In The Bluetooth Host Layer In Linux, Jack Dibari

Cybersecurity Undergraduate Research Showcase

This paper investigates vulnerabilities within the Bluetooth host layer in Linux systems. It examines the Bluetooth protocol's evolution, focusing on its implementation in Linux, particularly through the BlueZ host software. Various vulnerabilities, including BleedingTooth, BLESA, and SweynTooth, are analyzed.

The Security Of Deep Neural Networks, Jalaya Allen

Cybersecurity Undergraduate Research Showcase

Our society has transitioned from our primitive lifestyle to soon, an increasingly automatic one. That idea is further exemplified as we shift into an AI era, better known as Artificial intelligence. Artificial Intelligence is classified as computer systems that can perform tasks that typically require human intelligence. However, a common thought or question that most might have is, how is this done? How does AI process information the way we want it to and have access to so much information? AI is trained by systems called AI models. These modeling programs are trained on data to recognize patterns or make …

High-Resolution And Quality Settings With Latent Consistency Models, Steven Chen, Junrui Zhang, Rui Ning

Cybersecurity Undergraduate Research Showcase

Diffusion Models have become powerful generative models which is capable of synthesizing high-quality images across various domains. This paper explores Stable Diffusion and mostly focuses on Latent Diffusion Models. Latent Consistency Models can enhance the inference with minimal iterations. It demonstrates the performance in image in-painting and class-conditional synthesis tasks. Throughout the experiment different datasets and parameter configurations, the paper highlights the image quality, processing time, and parameter. It also discussed the future directions including adding trigger-based implementation and emotional-based themes to replace the prompt.

Data Profits Vs. Privacy Rights: Ethical Concerns In Data Commerce, Amiah Armstrong

Cybersecurity Undergraduate Research Showcase

In today’s digital age, the collection and sale of customer data for advertising is gaining a growing number of ethical concerns. The act of amassing extensive datasets encompassing customer preferences, behaviors, and personal information raises questions of its true purpose. It is widely acknowledged that companies track and store their customer’s digital activities under the pretext of benefiting the customer, but at what cost? Are users aware of how much of their data is being collected? Do they understand the trade-off between personalized services and the potential invasion of their privacy? This paper aims to show the advantages and disadvantages …

Study Of Deep Learning Models To Classify Nasa’S Kepler Light Curves, Heena Minnich

Computer Science Theses & Dissertations

The search for exoplanets has been an ongoing effort since the first discoveries of planets beyond our solar system in the 1990s. Finding a potentially habitable planet outside our solar system could provide key insights on life elsewhere in the universe. NASA Missions such as the Kepler, launched in 2009 and completed in 2018, have provided a massive amount of data in this goal by using the transit method to discover repetitive and periodic dips in visible light around a star. The transit method has been used to measure flux, the brightness of a star over time. These flux time …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

A Trustworthy Self-Sovereign Data And Identity Management Framework, Efat Fathalla

Electrical & Computer Engineering Theses & Dissertations

Data is a fundamental building block in the digital world, providing a basis for decision making and growth across numerous applications. In our modern world, we have become accustomed to collecting data on everything, including devices, machines, and people. The increased value of such data has led to aggressive harvesting mechanisms that prioritize data collection, storage, and pervasiveness while often disregarding security, privacy concerns, and compliance with regulations and standards. Such a pervasive attitude towards data has resulted in a loss of control, prompting concerns among individuals and mobilizing the scientific community towards advocating for data self-sovereignty.

Self-Sovereign Identity (SSI) …

Scaled And Graduated Learning In Deep Relu Networks And Reconstructing Depp Inelastic Scattering Kinematics, Abdullah Ayar Farhat

Mathematics & Statistics Theses & Dissertations

To address computational challenges in learning deep neural networks, properties of deep RELU networks were studied to develop a multi-scale learning model. The multi-scale model was compared to the multi-grade learning models. Unlike the deep neural network learned from the standard single-scale, single-grade model, the multi-scale neural networks use low scale information from all hidden layers, and thusly provide a robust approximation method that requires fewer parameters, lower computational time, and is resistant to noise. It is shown that the multiscale method is not subject to issues arising from the vanishing gradient problem. This allows very deep multi-scale networks to …

Auditory Vigilance Decrement In Drivers Of A Partially Automated Vehicle: A Pilot Study Using A High-Fidelity Driving Simulator, Luca Brooks, Jeffrey Glassman, Yusuke Yamani

Undergraduate Research Symposium

Vigilance decrement is the decline in the ability to monitor and detect behaviorally important signals over time, a phenomenon that can arise even after 30 minutes of watch (Mackworth, 1948). Recently, McCarley & Yamani (2021) found bias shifts, sensitivity losses, and attentional lapses contribute to vigilance decrement, but when each effect is isolated, there was little evidence that sensitivity loss affected vigilance decrement. With the introduction of partially autonomous vehicles, vigilance decrement may be problematic for drivers who must monitor the autonomous system for failures and takeover requests. Thus, this pilot study aims to extend McCarley and Yamani (2021) and …

Improving Educational Delivery And Content In Juvenile Detention Centers, Yomna Elmousalami

Undergraduate Research Symposium

Students in juvenile detention centers have the greatest need to receive improvements in educational delivery and content; however, they are one of the “truly disadvantaged” populations in terms of receiving those improvements. This work presents a qualitative data analysis based on a focus group meeting with stakeholders at a local Juvenile Detention Center. The current educational system in juvenile detention centers is based on paper worksheets, single-room style teaching methods, outdated technology, and a shortage of textbooks and teachers. In addition, detained students typically have behavioral challenges that are deemed "undesired" in society. As a result, many students miss classes …

A Comparison Of Machine Learning Surrogate Models Of Street-Scale Flooding In Norfolk, Virginia, Diana Mcspadden, Steven Goldenberg, Binata Roy, Malachi Schram, Jonathan L. Goodall, Heather Richter

Community & Environmental Health Faculty Publications

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …

A Review Of Hybrid Cyber Threats Modelling And Detection Using Artificial Intelligence In Iiot, Yifan Liu, Shancang Li, Xinheng Wang, Li Xu

Information Technology & Decision Sciences Faculty Publications

The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest …

It Is Not Only About Having Good Attitudes: Factor Exploration Of The Attitudes Toward Security Recommendations, Miguel A. Toro-Jarrin, Pilar Pazos, Miguel A. Padilla

Engineering Management & Systems Engineering Faculty Publications

Numerous factors determine information security-related actions (IS-actions) in the workplace. Attitudes toward following security rules and recommendations and attitudes toward specific IS actions determine intentions associated with those actions. IS research has examined the role of the instrumental aspect of attitudes. However, authors argue that attitudes toward a behavioral object are a multidimensional construct. We examined the dimensionality of attitudes toward security recommendations, hypothesized its multidimensional nature, and developed a new scale [attitudes toward security recommendations (ASR scale)]. The results indicated the multidimensional nature of attitudes toward security recommendations supporting our hypothesis. The results revealed two dimensions corresponding to the …

Reinforcement Learning For Optimal Kicking Actions In Humanoid Robotics: Advancing Robotic Autonomy And Versatility, Suresh Dodda, Sathish Kumar Chintala, Sukender Reddy Mallreddy, Sharath Chandra Macha, Yashwanth Vasa, Sapan Bharadwaj Bonala, Navin Kamuni, Sujatha Alla

Engineering Management & Systems Engineering Faculty Publications

Acquiring the necessary skills to perform a work effectively and efficiently requires a significant investment of time and computing power. Previous applications of Reinforcement Learning (RL) for action optimization in humanoid robotics have shown how promising this technology is for moving robotics towards true autonomy and versatility. Therefore, this study offers the first use of RL to create an entirely optimal kicking action for the Alderbaran Nao robot. Kicking motions that were steady, precise, quick, and able to kick farther than any existing RoboCup squad were generated by optimizing for a multi-objective reward function. We demonstrate that the ideal kicking …

Data Driven Trade-Off Analysis For Cybersecurity, Goskel Kucukkaya, Murat Ozer, Murat Balci, Emrah Ugurlu

Engineering Management & Systems Engineering Faculty Publications

Trade-off analysis, a specialization of systems engineering, addresses design criteria like security, cost, performance, and compliance. Monte Carlo simulations are commonly employed to generate impact scenarios for trade-off analysis combined with solution alternatives that accommodate industry-specific considerations and uncertainties. In the cyber domain, this paper proposes a methodology for data-driven trade-off analysis in cybersecurity, leveraging industry reports as primary data sources using confidentiality, integrity, and availability as trade-off analysis objectives. Distribution functions are derived to manage and model uncertainties for various industries. The approach given in this study aims to facilitate informed choices and to enhance cybersecurity decision making and …

Tapped In: The Rise Of Mobile Malware, Chrystofuer Davenport

Cybersecurity Undergraduate Research Showcase

As the world of technology continues to evolve and become more advanced with our life, so do the dangers and threats that are determined to hinder that development. Malware continues to be a danger to internet surfers or people with access to technology. At first it was an issue that existed only with computers but since the evolution of smartphones in the early twenty-first century, mobile devices have been a target for multiple malware viruses. It’s important to be aware of what different viruses are capable of doing and how to avoid them when they are encountered. Even though computers …

Development Of A Positive Urinalysis Criteria Using A Machine Learning Approach, Kari Flicker, Jessica Parrott, Tammy Speerhas, Turaj Vazifedan, Theresa Guins, Jeffrey Bobrowtiz, Anne Mcevoy, Jade Eves, Debra Conrad, Benjamin Klick

Ellmer School of Nursing Faculty Publications

Background: Urinary tract infections (UTIs) are a commonly encountered diagnosis at pediatric urgent care (UC) centers. The urinalysis (UA) is usually the initial study in UC settings used to guide decisions regarding initiating empiric antibiotics and/or pursuing urine culture. However, studies in pediatric UC settings examining the ideal threshold for a positive result are lacking.

Methods: UA result data were extracted from the records of 6,327 pediatric patients, which were collected as part of a previous QI project. Logistic regression was used to determine the predictors of positive urine cultures. Decision trees for a positive UA result for both clean …

Sscm: A Secured Approach To Supply Chain Management Using Blowfish Optimization, Shitharth Selvarajan, Hariprasath Manoharan, Alaa O. Khadidos, Achyut Shankar, Adil O. Khadidos, Wattana Viriyasitavat, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

This study examines the importance of enterprise information systems that link several corporate organisations to share information about diverse products under high security settings. The primary goal of the proposed strategy is to create a direct link between product demand and production to minimise the impact of rising costs. The research motive to make a connection cannot be resolved without suitable data that shows both quantity and quality in each organisation unit. The suggested method is designed to deliver accurate data to authorised end users while preventing any data exposure to unauthorised users. Security cryptographic keys are utilised to create …

Predicting The Need For Cardiovascular Surgery: A Comparative Study Of Machine Learning Models, Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi

Engineering Management & Systems Engineering Faculty Publications

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques …

Asem 4.0/5.0 - Evolving The Engineering Management Profession Through Industry 4.0/5.0 Collaborative Networks, T. Steven Cotter, Faisal Mahmud, Ziniya Zahedi

Engineering Management & Systems Engineering Faculty Publications

The American Society for Engineering Management was created and matured under Industry 3.0 automation. The emergence of Industry 4.0 and 5.0 are forcing all organizational sectors to rethink their long-term strategy with respect to emerging horizontal/vertical cyber-physical systems integration. This leaves open the question of the directions in which ASEM should evolve into the 21st century. This paper reports an initial mapping of Industry 4.0 and 5.0 technologies and initiatives as goal-oriented, long-term strategic collaborative networks. The research method began with the Boston Consulting Group nine technologies of Industry 4.0 (2015) and the Industry 5.0 technologies within its human-centric, sustainability, …

Selecting And Evaluating Key Mds-Updrs Activities Using Wearable Devices For Parkinson's Disease Self-Assessment, Yuting Zhao, Xulong Wang, Xiyang Peng, Ziheng Li, Fengtao Nan, Menghui Zhuo, Jun Qi, Yun Yang, Zhong Zhao, Lida Xu, Po Yang

Information Technology & Decision Sciences Faculty Publications

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities combination scheme based …

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …