Computer Sciences Commons^™

Predicting The Pebcak: A Quantitative Analysis Of How Cybersecurity Education, Literacy, And Awareness Affect Individual Preparedness., Annie Goodman

Theses/Capstones/Creative Projects

This essay explores the relationship between individuals' cybersecurity education, literacy, awareness, and preparedness. While cybersecurity is often associated with complex hacking scenarios, the majority of data breaches and cyber-attacks result from individuals inadvertently falling prey to phishing emails and malware. The lack of standardized education and training in cybersecurity, coupled with the rapid expansion of technology diversity, raises concerns about individuals' cybersecurity preparedness. As individuals are the first line of defense and the weakest link in cybersecurity, understanding the influence of education, literacy, and awareness on their adherence to best practices is crucial. This work aims to survey a diverse …

A Comprehensive Review On Machine Learning In Healthcare Industry: Classification, Restrictions, Opportunities And Challenges, Qi An, Saifur Rahman, Jingwen Zhou, James Jin Kang

Research outputs 2022 to 2026

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning …

Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer

School of Cybersecurity Faculty Publications

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …

Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman

Graduate Theses and Dissertations

Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …

Practical Indirect Control Flow Analysis For Binary Executables, Haotian Zhang

Computer Science and Engineering Dissertations - Archive

Resolving indirect control flow is one of the fundamental challenges in binary analysis. Improving the accuracy of the indirect control flow analysis is vital to the binary analysis domain. Many analysis algorithms and security techniques rely on a precise indirect control flow result, such as recursive disassembling, control flow integrity, data-flow analysis, etc. Incorrect or even inaccuracy indirect control flow analysis results can compromise or even break the assumptions of these analyses. This thesis explores this topic from two directions, altering the indirect control flow analysis to make it more suitable for different scenarios and improving the accuracy of indirect …

Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera

Computer Science and Engineering Dissertations - Archive

Deep learning has emerged as an increasingly valuable tool, employed across a myriad of applications. However, the intricacies of deep learning systems, stemming from their sensitivity to specific network architectures, have rendered them challenging for non-experts to harness, thus highlighting the need for automatic network architecture optimization. Prior research predominantly optimizes a network for a single problem through architecture search, necessitating extensive training of various architectures during optimization.\\ To tackle this issue and unlock the potential for transferability across tasks, this dissertation presents a groundbreaking approach that employs Reinforcement Learning to develop a network optimization policy based on an abstract …

Sparse Format Conversion And Code Synthesis, Tobi Goodness Popoola

Boise State University Theses and Dissertations

Sparse computations are important in scientific computing. Many scientific applications compute on sparse data. Data is said to be sparse if it has a relatively small number of non-zeros. Sparse formats use auxiliary arrays to store non-zeros, as a result, the contents of auxiliary arrays are not known until run-time. The Inspector/Executor (I/E) paradigm uses run-time information for compiler optimizations. An inspector computes information at run-time to drive transformations. The executor---a compile-time transformation of the original code--- uses information computed by the inspector. The sparse polyhedral framework (SPF) encompasses a series of tools to support I/E run-time transformations. This work …

Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale

Graduate Theses and Dissertations

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain …

On The Predictability Of Appropriate Prosody Of Dialog Markers Directly From The Local Context, Anindita Nath

Open Access Theses & Dissertations

Today's state-of-the-art spoken dialog systems lack context-appropriate prosody in their responses, often making them sound unnatural. Better modeling of this contextual dependency would enable natural prosodic responsiveness. Accordingly, this dissertation explores the extent to which the prosody of a dialog marker can be predicted directly from the prosody of its local context. The prediction performance was evaluated in terms of the similarity between the predicted and the observed prosodic features as measured by the reduction of root mean square error from the baseline. This prediction task was accomplished for multiple combinations of various sets of context features and different machine …

Analyzing Software Maintenance Through Machine Learning And Mining Software Repositories Approaches, Sayed Mohsin Reza

Open Access Theses & Dissertations

The rapid growth of software systems demands meticulous planning and maintenance to accommodate the evolution of the code base over extended periods. Without maintenance, software systems will become more complex, low in quality, and hence unsustainable. Software engineers who perform maintenance often strive to optimize code quality or minimize code smells in a timely manner. Several techniques have been used to detect code quality or code smells as a part of software maintenance. Most of these techniques are based on heuristics, which create detection rules using a few metrics. These approaches have reasonable accuracy but do not work in cross-project …

A Framework To Build Secure Microservice Architecture, Wai Yan Elsa Tai Ramirez

Open Access Theses & Dissertations

Microservice architecture has become a popular architecture style in recent years. According to a series of surveys conducted by IBM Market Development & amp; Insights in 2021, microservices are heavily used in many industries worldwide. With an increase in the adoption of microservice architecture in the development of applications, such as Netflix, Amazon, Uber, Ebay, Twitter, DoorDash, Capital One, and Monzo, and the increase in security breaches in microservice based systems (e.g., the DoorDash data breaches in 2019 and 2022, Twitter data breach in 2022, and compromises to Netflixâ??s infrastructure), there is a need to examine and understand security issues …

Detecting Complex Cyber Attacks Using Decoys With Online Reinforcement Learning, Marcus Gutierrez

Open Access Theses & Dissertations

Most vulnerabilities discovered in cybersecurity can be associated with their own singular piece of software. I investigate complex vulnerabilities, which may require multiple software to be present. These complex vulnerabilities represent 16.6% of all documented vulnerabilities and are more dangerous on average than their simple vulnerability counterparts. In addition to this, because they often require multiple pieces of software to be present, they are harder to identify overall as specific combinations are needed for the vulnerability to appear.

I consider the motivating scenario where an attacker is repeatedly deploying exploits that use complex vulnerabilities into an Airport Wi-Fi. The network …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Supporting Account-Based Queries For Archived Instagram Posts, Himarsha R. Jayanetti

Computer Science Theses & Dissertations

Social media has become one of the primary modes of communication in recent times, with popular platforms such as Facebook, Twitter, and Instagram leading the way. Despite its popularity, Instagram has not received as much attention in academic research compared to Facebook and Twitter, and its significant role in contemporary society is often overlooked. Web archives are making efforts to preserve social media content despite the challenges posed by the dynamic nature of these sites. The goal of our research is to facilitate the easy discovery of archived copies, or mementos, of all posts belonging to a specific Instagram account …

Opportunities And Challenges From Major Disasters Lessons Learned Of Long-Term Recovery Group Members, Eduardo E. Landaeta

Graduate Program in International Studies Theses & Dissertations

Natural hazards caused by the alteration of weather patterns expose populations at risk, with an outcome of economic loss, property damage, personal injury, and loss of life. The unpredictability of disasters is a topic of concern to most governments. Disaster policies need more attention in aligning mitigation opportunities with disaster housing recovery (DHR). The effect of flooding, which primarily impacts housing in coastal areas, is one of the most serious issues associated with natural hazard. Flooding has a variety of causes and implications, especially for vulnerable populations who are exposed to it. DHR is complex, involving the need for effective …

Design, Modeling, And Simulation Of Secure X.509 Certificate Revocation, Sai Medury

Masters Theses and Doctoral Dissertations

TLS communication over the internet has risen rapidly in the last seven years (2015--2022), and there were over 156M active SSL certificates in 2022. The state-of-the-art Public Key Infrastructure (PKI), encompassing protocols, computational resources, and digital certificates, has evolved for 24 years to become the de-facto choice for encrypted communication over the Internet even on newer platforms such as mobile devices and Internet-of-Things (IoT) (despite being low powered with computational constraints). However, certificate revocation is one sub-protocol in TLS communication that fails to meet the rising scalability demands and remains open to exploitation. In this dissertation, the standard for X.509 …

Development Of A Cost-Constrained Intelligent Prosthetic Knee With Real-Time Machine Learning, Predictive Stumble Control, Lucas Jonathan Galey

Open Access Theses & Dissertations

The field of biomechatronics is evolving quickly with advances in computer science, biology, and electrical and mechanical engineering. Coupled with increased interests in machine learning (ML) across all industry sectors, there are opportunities to leverage advanced analytics in uniquely complex problems. This study aimed to deploy real-time ML predictions in a novel microprocessor-controlled prosthetic knee (MPK) device capable of identifying and responding to stumble-events to reduce amputee fall prevalence. Innately, stumbling is a chaotic event. Current MPKs operate by detecting gait characteristics and reacting to preprogrammed states. While these systems are beneficial in significant ways, such as energy expenditure and …

Thermal Behavior Of Plain And Fiber-Reinforced Rigid Concrete Airfield Runways, Arash Karimi Pour

Open Access Theses & Dissertations

The environmental condition and temperature gradient are important factors resulting in concrete airfield runways cracking during the time. Rigid concrete airfield runways experience different thermal gradients during the day and night due to changes in air temperature. Curling and thermal expansion stresses are the main consequences resulting in various types of cracking over the surface and thickness of concrete airfield runways and increasing maintenance costs. The curvature of concrete slabs increases with an increase in the temperature gradient which is amplified when runways open to traffic. Additionally, the combination of the curling and shrinkage stresses, in rare circumstances, can be …

Advancing Iot Security Through Blockchain-Based Decentralized Platform And Ai-Powered Digital Forensics, Ruipeng Zhang

Masters Theses and Doctoral Dissertations

The proliferation of Internet of Things (IoT) devices, from smartphones, smart thermostats to smart home security systems, is revolutionizing our society and daily lives. However, it also has posed significant challenges to IoT security and forensics. To tackle those challenges, innovative solutions are designed to enhancing IoT security and accelerating investigation of cybersecurity incidents by leveraging recent technological advancements in Blockchain and Artificial Intelligence (AI). First, an IoT service platform, called DISP, is proposed to improve the security and interoperability of IoT systems. DISP utilizes the consortium blockchain technology to transform centralized, insecure IoT communications into decentralized, secure, and traceable …

A Brascamp-Lieb–Rary Of Examples, Anina Peersen

Mathematics, Statistics, and Computer Science Honors Projects

This paper focuses on the Brascamp-Lieb inequality and its applications in analysis, fractal geometry, computer science, and more. It provides a beginner-level introduction to the Brascamp-Lieb inequality alongside re- lated inequalities in analysis and explores specific cases of extremizable, simple, and equivalent Brascamp-Lieb data. Connections to computer sci- ence and geometric measure theory are introduced and explained. Finally, the Brascamp-Lieb constant is calculated for a chosen family of linear maps.

Analysis Of Post-Translational Modifications (Ptm) Crosstalk, Amit Das

Theses and Dissertations

Mass spectrometry-based proteomics is a powerful tool for identifying post-translational modifications (PTMs) across the proteome. O-GlcNAcylation and phosphorylation are two PTMs that play crucial roles in regulating cellular processes, including cardiac contractile function. Dysregulation of these PTMs has been implicated in the development and progression of diabetic cardiomyopathy. In this study, we aimed to investigate the interplay between O-GlcNAcylation and phosphorylation in healthy and type 2 diabetic hearts, with a specific focus on the functional relationships between these PTMs and their potential therapeutic implications.

Utilizing mass spectrometry data, we identified and quantified specific PTMs on myofilament proteins, uncovering 1354 O-GlcNAcylated …

Self-Supervised Representation Learning For Motion Time Series: A Case Study In Activity Recognition, Luis Carlos Garza Perez

Theses and Dissertations

In this thesis we will learn about what contrastive learning and time series are and understand the differences between supervised and self-supervised frameworks in machine learning. In addition, we will describe how the newest and most efficient self-supervised learning framework for visual representations to this date works, called SimCLR, which was originally developed to obtain useful vector representations from static images. We will also explain what TS2Vec is, and how a combination of both approaches can be applied to the concept of a time series, and still be able to extract a vector representation of the subject described by the …

The Effect Of Cybersecurity Training On Government Employee’S Knowledge Of Cybersecurity Issues And Practices, Juan Jaime Saldana Ii

Theses and Dissertations

There is an ever-pressing need for cybersecurity awareness and implementation of learning strategies in the workplace to mitigate the increased threat posed by cyber-attacks and exacerbated by an untrained workforce. The lack of cybersecurity knowledge amongst government employees has increased to critical levels due to the amount of sensitive information their agencies are responsible for. The digital compromise of a government entity often leads to a compromise of constituent data along with the disruption of public services (Axelrod, 2019; Yazdanpanahi, 2021). The need for awareness is further complicated by agencies looking to cater to a digital culture looking for a …

Problems In Algorithmic Self-Assembly And A Genetic Approach To Patterns, Andrew Rodriguez

Theses and Dissertations

As it becomes increasingly harder to make transistors smaller, replacements for traditional silicon computers become sought after. To study the computing power of these potential computers, various theoretical models have been proposed, such as the abstract Tile Assembly Model (aTAM) and chemical reaction networks (CRNs). This thesis compiles research in various models such as the aTAM, Tile Automata, and CRNs. This work shows an investigation of covert computation in the aTAM and an evolutionary algorithm to approximate solutions to the pattern self-assembly tile set synthesis (PATS) problem. Next, optimal state complexity for building squares in Tile Automata is shown along …

A Programmatic Geographic Information Systems Analysis Of Plant Hardiness Zones, Andrew Bowen

Electronic Theses and Dissertations

The Plant Hardiness Zone Map consists of thirteen geographical zones that describe whether a plant can survive based on average annual minimal temperatures. As climate change progresses, minimum temperatures in all regions are expected to change. This work programmatically evaluates predicted future climate projection data and converts it to United States Department of Agriculture-defined hardiness zones. Through the next 80 years, hardiness zones are projected to move poleward; in effect, colder zones will lose area and warmer zones will gain area globally. Some implications include changes in crop growing degree days, which could alter crop productivity, migration and settlement of …

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers, Ian L. Grisham

Electronic Theses and Dissertations

The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not …

Ncq: Code Reuse Support For Node.Js Developers, Brittany Reid, Marcelo D'Amorim, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related …

What's Behind Tight Deadlines? Business Causes Of Technical Debt, Rodrigo Rebouças De Almeida, Christoph Treude, Uirá Kulesza

Research Collection School Of Computing and Information Systems

What are the business causes behind tight deadlines? What drives the prioritization of features that pushes quality matters to the back burner? We conducted a survey with 71 experienced practitioners and did a thematic analysis of the openended answers to the question: “Could you give examples of how business may contribute to technical debt?” Business-related causes were organized into two categories: pure-business and business/IT gap, and they were related to ‘tight deadlines’ and ‘features over quality’, the most frequently cited management reasons for technical debt. We contribute a cause-effect model which relates the various business causes of tight deadlines and …

Applying Information Theory To Software Evolution, Adriano Torres, Sebastian Baltes, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

Although information theory has found success in disciplines, the literature on its applications to software evolution is limit. We are still missing artifacts that leverage the data and tooling available to measure how the information content of a project can be a proxy for its complexity. In this work, we explore two definitions of entropy, one structural and one textual, and apply it to the historical progression of the commit history of 25 open source projects. We produce evidence that they generally are highly correlated. We also observed that they display weak and unstable correlations with other complexity metrics. Our …

Two Sides Of The Same Coin: Exploiting The Impact Of Identifiers In Neural Code Comprehension, Shuzheng Gao, Cuiyun Gao, Chaozheng Wang, Jun Sun, David Lo, Yue Yu

Research Collection School Of Computing and Information Systems

Previous studies have demonstrated that neural code comprehension models are vulnerable to identifier naming. By renaming as few as one identifier in the source code, the models would output completely irrelevant results, indicating that identifiers can be misleading for model prediction. However, identifiers are not completely detrimental to code comprehension, since the semantics of identifier names can be related to the program semantics. Well exploiting the two opposite impacts of identifiers is essential for enhancing the robustness and accuracy of neural code comprehension, and still remains under-explored. In this work, we propose to model the impact of identifiers from a …