Computer Sciences Commons^™

Integrating Criminological Theories In Cybersecurity Risk Assessment: A Study Of The Traci Framework's Application To Critical Infrastructure, Connor S. Martin

Doctoral Dissertations and Projects

This dissertation explores the application of the Taxonomy for Risk Assessment of Cyberattacks on Critical Infrastructure (TRACI) framework, a tool designed to systematically evaluate cybersecurity threats against critical infrastructure. TRACI integrates principles from Routine Activity and Rational Choice Theories to provide a detailed and comprehensive understanding of cybersecurity risks. This integration facilitates an in-depth analysis not only of how cyberattacks occur but also of the underlying reasons they are initiated, by categorizing and assessing risks based on factors such as attacker motivations and systemic vulnerabilities. By employing ANOVA to assess variations in risk assessment scores across TRACI's designated categories—Assets, Risk …

Safety And Optimality Monitors For Learning-Enabled Systems Using Conformal Prediction, Jackson Cox

McKelvey School of Engineering Graduate Student Theses & Dissertations

The use of machine learning to create data-driven plant models and controllers has led to an increased need for safety and optimality monitors for model-based systems. System plant models are subject to uncertainty due to learning constraints such as unseen data and overfitting or physical constraints such as unknown dynamics and noise. This uncertainty is detrimental to safety-critical systems and must be properly regulated. To curb this uncertainty, we create prediction sets using the guarantees provided by Conformal Prediction. With a user-specified high probability, these prediction sets contain the true plant system states for an entire prediction horizon, which we …

Schedulability Analysis Of Multi-Phase Limited-Preemption Tasks, Benjamin Standaert

McKelvey School of Engineering Graduate Student Theses & Dissertations

This work addresses hard real-time systems, in which tasks must be scheduled so that they are guaranteed to meet deadlines. In particular, when tasks execute across multiple domains with high preemption costs, the combined cost of these preemptions can cause the system to become unschedulable. The number of preemptions must therefore be bounded to limit the overall task execution time, while ensuring that task blocking times are small enough to allow the system to be schedulable. Prior work introduces the Multi-Phase Secure model, which describes a more exact version of this scenario, and an algorithm to determine schedulability of sporadic …

Feasibility Of Large Language Models For Ceus Li-Rads Categorization Of Small Liver Nodules In Patients At Risk For Hepatocellular Carcinoma, Jiayan Huang, Rui Yang, Xiaotong Huang, Keyu Zeng, Yan Liu, Jun Luo, Andrej Lyshchik, Qiang Lu

Department of Radiology Faculty Papers

BACKGROUND: Large language models (LLMs) offer opportunities to enhance radiological applications, but their performance in handling complex tasks remains insufficiently investigated.

PURPOSE: To evaluate the performance of LLMs integrated with Contrast-enhanced Ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) in diagnosing small (≤20mm) hepatocellular carcinoma (sHCC) in high-risk patients.

MATERIALS AND METHODS: From November 2014 to December 2023, high-risk HCC patients with untreated small (≤20mm) focal liver lesions (sFLLs), were included in this retrospective study. ChatGPT-4.0, ChatGPT-4o, ChatGPT-4o mini, and Google Gemini were integrated with imaging features from structured CEUS LI-RADS reports to assess their diagnostic performance for sHCC. …

Enabling Per-File Data Recovery From Ransomware Attacks Via File System Forensics And Flash Translation Layer Data Extraction, Josh Dafoe, Niusen Chen, Bo Chen, Zhenlin Wang

Michigan Tech Publications

Ransomware attacks are increasingly prevalent in recent years. Crypto-ransomware corrupts files on an infected device and demands a ransom to recover them. In computing devices using flash memory storage (e.g., SSD, MicroSD, etc.), existing designs recover the compromised data by extracting the entire raw flash memory image, restoring the entire external storage to a good prior state. This is feasible through taking advantage of the out-of-place updates feature implemented in the flash translation layer (FTL). However, due to the lack of “file” semantics in the FTL, such a solution does not allow a fine-grained data recovery in terms of files. …

Microservice Architecture For Social Media Data Collection, Analysis, And Dashboarding, Sai Ram Manohar Koya

Theses and Dissertations

This research presents a novel methodology for the collection, processing, and analysis of social media data using a microservices-based architecture. The proposed system integrates multiple data streams from various social media platforms, transforming this information into a unified, JSON-based DataObject model for seamless processing and analysis. Unlike monolithic architectures, the microservices approach offers scalability and flexibility, allowing the system to handle the high velocity, variety, and volume of unstructured social media data, including text, images, and videos. By leveraging NoSQL databases like MongoDB, the methodology efficiently manages data in a semi-structured format, supporting real-time analytics such as sentiment analysis, toxicity …

Exploration Of The Gap Between The Secure Web Application Development Competencies Needed By Industry And Those Competencies Provided By Graduates Of U.S. Undergraduate Software Engineering Programs, Gary Allen Harris

Theses and Dissertations

Literature demonstrates that threats and attacks on computer systems and networks have been around since the beginning of computing, and the number, severity, sophistication, and costs of attacks and data breaches are continuing to grow. Several studies suggest that one of the most common causes of data breaches is insecure web applications that contain vulnerable application code. These studies suggest that poor secure web application development practices are a prime cause of the susceptible web applications. Additionally, studies suggest that higher education is not meeting industry’s secure software/web application development needs. Employers have reported that they are not getting the …

Artificial Intelligence-Based Methodologies For Early Diagnostic Precision And Personalized Therapeutic Strategies In Neuro-Ophthalmic And Neurodegenerative Pathologies, Rahul Kumar, Ethan Waisberg, Joshua Ong, Phani Paladugu, Dylan Amiri, Jeremy Saintyl, Jahnavi Yelamanchi, Robert Nahouraii, Ram Jagadeesan, Alireza Tavakkoli

SKMC Student Presentations and Publications

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and …

The Chinese Room And Creating Consciousness: How Recent Strides In Ai Technology Revitalize A Classic Debate, Thomas Held

Departmental Honors & Graduate Capstone Projects

Since 1950, when Alan Turing first posed the question of whether a machine could think, the possibility of artificial consciousness has sparked intense and ongoing debate, and strong positions have been staked out on each side of the argument. On the one hand, the historically popular functionalist school of thought claims that any system capable of producing suitably “conscious” behavior in a given environment should be considered conscious. On the other hand, John Searle’s famous “Chinese Room” argument insists that this cannot be the case, and that consciousness is in all likelihood not artificially reproducible. However, both positions have issues—the …

Improving Clinical Information Extraction From Electronic Health Records: Leveraging Large Language Models And Evaluating Their Outputs, Kriti Bhattarai

McKelvey School of Engineering Graduate Student Theses & Dissertations

Accurate extraction of clinical entities and phenotypes from unstructured electronic health record (EHR) text is crucial for various clinical research tasks, including cohort identification, tracking temporal patterns in disease progression and deciding treatment course. However, this task remains challenging due to the complexity and ambiguity of medical language. This dissertation explores the application of advanced generative pre-trained transformer (GPT) models, such as GPT-4, GPT-3.5-turbo, Llama-3.1, Llama-3 and Flan-T5, for clinical entity and phenotype extraction from EHRs. Building upon these findings, this dissertation also investigates a hybrid approach where integration of external knowledge sources, such as Unified Medical Language System (UMLS) …

Implication Of Generative Ai On Education And Research, Riddhi Gupta

The Journal of Purdue Undergraduate Research

No abstract provided.

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

An Examination Of Academic Library Platforms And Systems During Covid-19, Cole Hudson, Paul Gallagher

University Libraries Faculty Publications and Presentations

This paper examines the use and subsequent trajectory of academic library technologies due to the impact of the COVID-19 pandemic. Taking a broad view of technologies, the systems and services discussed will center around resource use because COVID restrictions shuttered many in-person technologies. The two academic libraries compared in this study show a similar pattern of use and signal growth of certain platforms and technologies for the future.

Efficient Visual Data Processing Approaches To Improve Resource Utilization, Yeganeh Jalalpour

Dissertations and Theses

With continuous advancements in technology, the volume of visual data being captured, distributed, and consumed across various applications is rapidly increasing. Enhancing the efficiency of visual data processing can improve resource consumption, making the storage, transmission, and use of this growing visual content more effective.

Image and video data constitute a significant share of internet traffic, making video compression a critical factor in enhancing overall data throughput by improving the video compression ratio. Capturing, transferring, and storing raw video data is challenging due to the substantial resources required for both storage and computation. Video compression, however, can significantly mitigate these …

Utilizing Large Language Models To Synthesize Product Desirability Datasets, John D. Hastings, Sherri Weitl-Harms, Joseph Doty, Zachary L. Myers, Warren Thompson

Research & Publications

This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor …

3d Game: Enhancing Game Ai With Machine Learning, Nicholas William English

Masters Projects

Machine learning (ML) and artificial intelligence (AI) are terms that are often used synonymously, but they are ever-so slightly different. Machine learning is really a subset of artificial intelligence and involves creating an algorithm so that a computer can learn patterns. Artificial intelligence extends machine learning with the goal to go beyond pattern recognition by having a computer that is capable of mimicking human intelligence. Video games often use the term AI in reference to the bots or non-playable characters (NPCs) that players may interact with. Generally, these bots do not actually implement AI, nor do they use ML, but …

Gpu-Accelerated Community Detection: Performance Comparison Of Networkx And Cugraph, Venkata Satyanarayana Pulaparthi

Masters Projects

Community detection in complex networks is an essential process in the field of network science, offering insights into the underlying structure and functionality of interconnected systems. As the scale and complexity of networks grows, traditional CPU-based methods for community detection struggle to keep pace, leading to the exploration of GPU-accelerated solutions.

This project investigates the implementation of the Louvain algorithm for community detection using GPU-accelerated computing via the CuGraph library. By comparing it too traditional CPU based methods implemented with NetworkX, this study examines performance improvements, scalability, and applicability across real-world datasets. Two networks were used for experimentation: Zachary’s Karate …

An Energy Resource Management For Cluster Based Iohv Supported By Fog Computing, Ahmed Jawad Kadhim

Karbala International Journal of Modern Science

Internet of Hybrid Vehicle Networks (IoHV) is a network generated by merging the Internet with a Hybrid Vehicular Ad-Hoc Network (H-VANET). In IoHV, various types of electric and fuel vehicles create tasks. However, executing several tasks by electric vehicles affects their lifetime because they suffer from energy limitation issues which is one of the IoHV challenges. On the other hand, fuel vehicles and fog nodes have unlimited energy and can be used to execute most tasks of electric vehicles quickly. In this paper, we produce a new Energy Resource management Technique for IoHV called ERTH that aims to offload the …

Effect Of Temperature And Rhenium Content In Precipitates On Dispersion Hardening Of Tungsten, Yulia R. Sharapova, Arseny M. Kazakov, Elena A. Korznikova, Alexandr Zinovev, Dmitry Terentyev, Sergey V. Dmitriev

Karbala International Journal of Modern Science

Tungsten (W) is being developed as a plasma-facing material for fusion reactors, where it is subjected to MeV neutron irradiation, low-energy helium isotope particles, and high temperatures. These conditions lead to the formation of point defects, dislocation loops, voids, and transmutation into rhenium (Re) and osmium (Os), which form precipitates that significantly impact dislocation motion and increase hardness. This study uses molecular dynamics modeling to examine the interaction between an edge dislocation and Re-rich particles of various stoichiometries, specifically coherent bcc-phase particles and noncoherent σ-phase precipitates. Results show that shear stress increases by approximately 20-40% with larger particle size (3-5 …

Calculation And Statistical Analysis Of Wins Above Replacement, Joshua Taylor

Departmental Honors & Graduate Capstone Projects

The Wins Above Replacement (WAR) statistic in Major League Baseball is a prominent metric used to estimate player value by quantifying all aspects of play in terms of wins added to a baseball team. We will use R to calculate WAR for all players from 1871 to 2012 and use data from those years to construct multivariate predictive models to attempt to estimate WAR for players from 2013 to 2024. We find strong correlations between predicted and actual WAR values for most models, with the exception of the polynomial predictive model for non-qualified pitchers.

Unmanned Aerial Systems (Uas) Image Preprocessing To Reduce Artifacts And Improve Geometric Registration When Generating Orthophoto Mosaics And 3d Models, Eddie Ironsmith

Electronic Theses and Dissertations

Drones can now be used to quickly collect imagery data in a highly automated way; however, individual images must be combined to form an orthomosaic or 3-Dimentional (3D) model using photogrammetry software. Currently, the existing software may generate erroneous output in the form of artifacts or positional errors caused by homogeneous areas, light reflections, object movement between photos, or sub-optimal algorithms. The goal of this research was to develop preprocessing algorithms that would filter movement (or other time or position-based differences) and areas of homogeneity. The hypothesis is that filtering these parts of the image would reduce artifacts and improve …

Advanced Models For Linking Process In Data Washing Machine, Bushra Sajid

Theses and Dissertations

Entity Resolution (ER) is a critical process in data integration and quality improvement that identifies and links multiple records referring to the same real-world entity. As data volumes and heterogeneity increase, traditional ER methods face new challenges, prompting research into more advanced techniques. The Proof-of-Concept Data Washing Machine (DWM), developed under the NSF DART Data Life Cycle and Curation research theme, aims to automatically detect and correct data quality errors through unsupervised entity resolution. Recent research focuses on enhancing DWM's effectiveness by replacing rule-based methods with machine learning and deep learning approaches, particularly in the linking process. Deep learning models, …

An Open-Source, Student-Centric Approach To The Cyber Kill Chain, Justin Lane Wooten

Theses and Dissertations

The cybersecurity landscape demands professionals with practical skills and a deep understanding of attack methodologies. However, many institutions face significant challenges in providing comprehensive cybersecurity education due to the high costs associated with commercial tools and platforms. This thesis presents a student-centric, open-source curriculum for teaching the Cyber Kill Chain, designed to bridge the gap between theoretical knowledge and real-world application while addressing the financial constraints faced by many educational institutions. Our approach leverages freely available tools and hands-on exercises to cover each phase of the Cyber Kill Chain, emphasizing ethical considerations and collaborative learning. We detail the curriculum development …

La Creatividad En Peligro: Como La Inteligencia Artificial Es Un Reto Para Los Artistas., Nathaly Cisneros

Capstones

Los artistas digitales han creado obras maestras que nos han dejado sin aliento con sus pinceles digitales, lápices y pinturas. Desde retratos que parecen saltar de la pantalla hasta paisajes que nos transportan a mundos desconocidos, su arte ha sido una fuente constante de inspiración.

Pero en los últimos años, una nueva fuerza ha comenzado a cambiar el juego. La inteligencia artificial ha estado avanzando a pasos agigantados y ahora se perfila como una amenaza para el futuro de los artistas digitales. ¿Qué significa esto para el arte y la creatividad?

Link: https://docs.google.com/document/d/1xe8UxDMekX_SwiIppyt_JppK8M-lB-YWNWGyeyShlJM/edit?usp=sharing

The Implementation Of Artificial Intelligence In University Classrooms: Perspective And Applications, Erika Grodzki, Gary Carlin, Stefanie Powers, Hung Chum Kao

Faculty and Staff Publications & Presentations

This study examined the integration of Artificial Intelligence (AI) in university classrooms, focusing on its benefits, challenges, and the diverse perspectives of academic faculty. While AI was widely embraced in disciplines like animation and design for enhancing creativity and efficiency, traditional fields remained cautious due to concerns about academic integrity and its impact on critical thinking. By analyzing literature and case studies, the presentation highlighted AI’s transformative potential in higher education, fostering dialogue on its strategic adoption to balance innovation with ethical and pedagogical considerations.

A Comprehensive Performance Evaluation Of Proprietary And Open-Source Language Models In Closed And Open-Domain Tasks, Abhilash Kanduri

Theses and Dissertations

As the field of Natural Language Processing (NLP) continues to evolve, evaluating the performance of both proprietary and open-source language models has become increasingly critical. This research provides a comprehensive analysis of proprietary models like GPT-3.5 Turbo, GPT-4, and GPT-4 Turbo, alongside open-source models such as FLAN-T5, GPT-Neo, and GPT-2. By leveraging traditional metrics like ROUGE and BLEU, as well as custom metrics including ReGrAde, Contextual Precision, and Faithfulness, the study evaluates these models across closed-domain tasks (e.g., factual question-answering) and open-domain tasks (e.g., creative writing and brainstorming). The proprietary models excelled in structured, fact-based tasks, while the open-source models …

Multi-Cloud Identity Security Utilizing Self-Sovereign Identity, Morgan Lee Reece

Theses and Dissertations

With the increasing use of multi-cloud environments, security professionals face challenges in configuration, management, and integration due to uneven security capabilities and features among providers. As a result, a fragmented approach toward security has been observed, leading to new attack vectors and potential vulnerabilities. Other research has focused on single-cloud platforms or specific applications of multi-cloud environments. Therefore, there is a need for a holistic security and vulnerability assessment and defense strategy that applies to multi-cloud platforms. This dissertation explores risk and vulnerability analysis to identify attack vectors from software, hardware, and the network, as well as interoperability security issues …

Analysis Of Cortical Evoked Auditory Response Detection In Adults Using Machine Learning, Pranavi Beerelli

Theses and Dissertations

This study focuses on the use of machine learning (ML) techniques to automate the detection of Cortical Evoked Auditory Responses (CEARs), which are key in understanding how the auditory cortex processes sound stimuli. Traditionally, analyzing these auditory responses has relied on manual interpretation by audiologists, a process that can introduce variability and human error, particularly in complex cases. To address this challenge, the research utilizes advanced deep learning models, including Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Bidirectional LSTM (BiLSTM) architectures, to analyze Electroencephalography (EEG) data and classify the presence or absence of auditory responses automatically. …

Deep Learning - Based Automated Detection And Classification Of Foreign Materials In Poultry Using Color And Hyperspectral Imaging, Rohini Maram

Theses and Dissertations

This thesis explores the use of Deep learning for detection and classification of small foreign materials (FMs) in poultry meat using color and hyperspectral imagery (HSI). The study employs You only look once (YOLO) object detection models on color images for precise localization, and one-dimensional convolutional neural network (1D CNN), two-dimensional convolutional neural network (2D CNN) was used on HSI (600 – 1700 nm) for classification. Twelve different FMs commonly known as polymers including PVC, PET, LDPE and HDPE, were examined using 52 color and 52 hyperspectral images. Four YOLO models (v5x, v7x, v8x, v10x) were implemented, trained, tested and …

Improved Vector Pruning For Partially Observable Markov Decision Processes, Thomas Jonathan Bowman

Theses and Dissertations

Exact dynamic programming algorithms for planning problems that are represented as partially observable Markov decision processes rely on a subroutine that removes, or ``prunes", dominated vectors from sets of vectors that represent piecewise-linear and convex value functions. The classic vector pruning subroutine solves one linear program per vector, where the number of variables is equal to the size of the state space and the number of constraints is equal to the number of vectors shown so far to be undominated. Thus, its scalability is limited not only by the number of linear programs it solves, but especially by their size. …