Digital Commons Network^™

Improving Ethics Surrounding Collegiate-Level Hacking Education: Recommended Implementation Plan & Affiliation With Peer-Led Initiatives, Shannon Morgan, Dr. Sanjay Goel

Military Cyber Affairs

Cybersecurity has become a pertinent concern, as novel technological innovations create opportunities for threat actors to exfiltrate sensitive data. To meet the demand for professionals in the workforce, universities have ramped up their academic offerings to provide a broad range of cyber-related programs (e.g., cybersecurity, informatics, information technology, digital forensics, computer science, & engineering). As the tactics, techniques, and procedures (TTPs) of hackers evolve, the knowledge and skillset required to be an effective cybersecurity professional have escalated accordingly. Therefore, it is critical to train cyber students both technically and theoretically to actively combat cyber criminals and protect the confidentiality, integrity, …

Using Digital Twins To Protect Biomanufacturing From Cyberattacks, Brenden Fraser-Hevlin, Alec W. Schuler, B. Arda Gozen, Bernard J. Van Wie

Military Cyber Affairs

Understanding of the intersection of cyber vulnerabilities and bioprocess regulation is critical with the rise of artificial intelligence and machine learning in manufacturing. We detail a case study in which we model cyberattacks on network-mediated signals from a novel bioreactor, where it is important to control medium feed rates to maintain cell proliferation. We use a digital twin counterpart reactor to compare glucose and oxygen sensor signals from the bioreactor to predictions from a kinetic growth model, allowing discernment of faulty sensors from hacked signals. Our results demonstrate a successful biomanufacturing cyberattack detection system based on fundamental process control principles.

Characterizing Advanced Persistent Threats Through The Lens Of Cyber Attack Flows, Logan Zeien, Caleb Chang, Ltc Ekzhin Ear, Dr. Shouhuai Xu

Military Cyber Affairs

Effective cyber defense must build upon a deep understanding of real-world cyberattacks to guide the design and deployment of appropriate defensive measures against current and future attacks. In this abridged paper (of which the full paper is available online), we present important concepts for understanding Advanced Persistent Threats (APTs), our methodology to characterize APTs through the lens of attack flows, and a detailed case study of APT28 that demonstrates our method’s viability to draw useful insights. This paper makes three technical contributions. First, we propose a novel method of constructing attack flows to describe APTs. This abstraction allows technical audiences, …

Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin

Military Cyber Affairs

Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.

Securing The Void: Assessing The Dynamic Threat Landscape Of Space, Brianna Bace, Dr. Unal Tatar

Military Cyber Affairs

Outer space is a strategic and multifaceted domain that is a crossroads for political, military, and economic interests. From a defense perspective, the U.S. military and intelligence community rely heavily on satellite networks to meet national security objectives and execute military operations and intelligence gathering. This paper examines the evolving threat landscape of the space sector, encompassing natural and man-made perils, emphasizing the rise of cyber threats and the complexity introduced by dual-use technology and commercialization. It also explores the implications for security and resilience, advocating for collaborative efforts among international organizations, governments, and industry to safeguard the space sector.

Commercial Enablers Of China’S Cyber-Intelligence And Information Operations, Ethan Mansour, Victor Mukora

Military Cyber Affairs

In a globally commercialized information environment, China uses evolving commercial enabler networks to position and project its goals. They do this through cyber, intelligence, and information operations. This paper breaks down the types of commercial enablers and how they are used operationally. It will also address the CCP's strategy to gather and influence foreign and domestic populations throughout cyberspace. Finally, we conclude with recommendations for mitigating the influence of PRC commercial enablers.

Modeling The Human Learning Process Using An Industrial Steam Boiler Analogy To Design A Psychophysiological-Based Hypermedia Adaptive Automation System, Liliana María Villavicencio López

USF Tampa Graduate Theses and Dissertations

This dissertation aims to address the existing gap in the integration of various dimensions within the student learning system, encompassing cognitive, emotional, and physical variables. The primary objective is to construct a Personalized Learning Adaptive Automation model using Electroencephalography (EEG) technology.

To provide deeper insight into the intricate nature of the Human Learning Process, this study introduces a novel analogy with an Industrial Steam Boiler. This analogy serves as a distinctive contribution to research in the field.

The research methodology involved the collection of brainwaves data from engineering students while they undertook educational tasks of varying levels of difficulty, categorized …

Advancing Depth-Storage-Discharge Modeling In Regional Hydrology, Fahad Alshehri

USF Tampa Graduate Theses and Dissertations

This dissertation presents the development of an innovative approach to populating rating characteristics and supporting hydrologic modeling, designed to simplify complex real-world hydrological systems and accurately estimate their responses to rainfall, runoff, baseflow and evaporation stresses. The core of this research addresses the challenges inherent in characterizing hydrography elements in hydrologic modeling, particularly in regions lacking comprehensive stream reach survey data, flow and stage. This issue is pronounced in areas with extensive wetland hydrography, where traditional modeling requires intensive manual calibration, and course rating data that are often unavailable. To overcome these challenges, this study introduces a novel procedure that …

Human Motion-Inspired Inverse Kinematics Algorithm For A Robotics-Based Human Upper Body Model, Urvish Trivedi

USF Tampa Graduate Theses and Dissertations

The goal of this research is to develop a human motion-inspired inverse kinematics algorithm framework specifically designed for a Robotics-Based Human Upper Body Model (RHUBM). This framework offers solutions to challenges in various fields. In humanoid robotics, the framework addresses the problem of unnatural robot movement by enabling the development of motion planning algorithms that incorporate human-like movements. For prosthetics, the framework tackles the challenge of amputee difficulty in learning and controlling prosthetics by providing a user-friendly interface that predicts and visualizes upper limb movements, enabling learning and practice. In rehabilitation therapy, the framework tackles …

Routing Problems Through The Lens Of Hybrid Algorithms, Sasan Mahmoudinazlou

USF Tampa Graduate Theses and Dissertations

This dissertation explores novel approaches to address complex combinatorial optimization challenges in transportation and routing scenarios. Three sets of contributions are presented, each encapsulated in a chapter. The first set of contributions introduces a pioneering hybrid genetic algorithm meticulously crafted to address the intricacies of the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP). These emerging problems involve the strategic use of both ground-based trucks and aerial drones for efficient package delivery. Our algorithm stands out by leveraging sophisticated chromosomes and dynamic programming, allowing for broad exploration by the genetic algorithm and effective exploitation …

Interfacial Magnetism And Anisotropy In Dirac And Weyl Semimetals, Noah Schulz

USF Tampa Graduate Theses and Dissertations

Semimetals have gained intense interest recently due to their exotic magnetic and electronic properties. One of the most widely studied semimetals is graphene, a Dirac semimetal. The utilization of graphene in devices and sensors requires interfacing it with other materials, which may induce potentially strong interfacial effects. Furthermore, graphene alone does not possess magnetic order. Studying the interfacial effects between graphene and magnetic materials is therefore of great importance in the application of graphene to meet modern technological needs. Furthermore, by understanding the fundamental interfacial physics between graphene and magnetic materials, new properties can be unlocked, broadening the possible applications …

Exploring The Use Of Enhanced Swad Towards Building Learned Models That Generalize Better To Unseen Sources, Brandon M. Weinhofer

USF Tampa Graduate Theses and Dissertations

Deep learning models, typically, take significant time to train. Classifier ensembles are areliable way to increase classifier accuracy and perhaps generalizability to unseen sources of data. These classifiers can be combined with a simple voting scheme. The problem is that having multiple models can very heavily increase training time. Snapshot ensembles have been shown to provide a boost in performance by creating an ensemble of classifiers with different weights during the training of a single deep learned model. This can somewhat solve the problem of the increased training time as you do not have to train separate models. As Machine …

An Analysis Of Driven Pile Relaxation In Florida Soils, Dalton E. Knowles

USF Tampa Graduate Theses and Dissertations

Prestressed concrete piles (PCP) are a common geotechnical foundational element employed in marine and land bridges. PCP are commonly driven by an impact diesel hammer until the desired nominal bearing resistance is reached at the end of the initial drive (EOID). At a later time, the pile resistance may be verified, but not always, via a restrike, usually with the same impact hammer. At the beginning of a restrike, the resistance of the pile is rarely equal to the resistance at the EOID either increasing, known as set-up, or decreasing, known as relaxation. Set-up is clearly beneficial, however, this thesis …

Dissolved Nitrogen Removal In Biochar Amended, High Permeability Media For Urban Stormwater Treatment, Mark Vicciardo

USF Tampa Graduate Theses and Dissertations

Nutrient pollution in stormwater drives the eutrophication of inland and costal waterbodies which leads to sea grass retreat and the proliferation of harmful algal blooms (HAB). These anthropogenic effects destabilize ecosystems, and some HABs can pose direct human health risk. Bioretention, or the storage and controlled discharge of stormwater run-off in an ecologically engineered setting, is a potential solution to this problem. However, it relies heavily on the settling of particles as a nutrient removal mechanism, and thus struggles with pollutants, such as dissolved nitrogen, which is a particular problem in Florida where the geological prominence of phosphorus leaves nitrogen …

Nutrient Removal Of Biochar Amended Modified Bioretention Systems Treating Nursery Runoff, Nicholas Richardson

USF Tampa Graduate Theses and Dissertations

Excess inputs of nutrients, such as nitrogen and phosphorus, to surface water bodies can lead to the presence of harmful algal blooms (HABs). HABs can damage their surrounding environments and the economies dependent on them. Nutrient pollution can be properly controlled and monitored at point-sources, such as wastewater treatment plants, but is more difficult to control for non-point sources, such as fertilizer runoff. One form of fertilizer runoff that remains overlooked is runoff from plant nurseries. Controlled release fertilizers (CRFs) embedded in plant containers at nurseries can leach excess nutrients into the runoff. For nurseries in Florida, best management practices …

Effects Of Unobservable Bus States On Detection And Localization Of False Data Injection Attacks In Smart Grids, Moheb Abdelmalak

USF Tampa Graduate Theses and Dissertations

In an era increasingly marked by sophisticated cyber-attacks, this thesis investigates the critical issue of bus unobservability in smart grids and its impact on the effectiveness of cyber-attack detection and localization models. Given that unobservability is a prevalent challenge in smart grids due to various factors, researchers have developed numerous algorithms for optimal Phasor Measurement Unit (PMU) placement under scenarios of limited observability. However, these models primarily focus on enhancing network observability, often without considering whether this placement optimally facilitates attack detection. This research is driven by the hypothesis that a deeper understanding of the effects of unobservable buses can …

Under Pressure: The Soft Robotic Clap-And-Fling Of Cuvierina Atlantica, Daniel Mead

USF Tampa Graduate Theses and Dissertations

Evolution over billions of years has led to unique animals of all types. The tiny sea butterfly is one that remains mostly anonymous because of its size and low place on the food chain, but it is a swimming creature that when examined closely reveals a surprise. Shockingly, it has a swimming motion nearly identical to small insects flying at intermediate Reynolds numbers, referred to as the clap-and-fling. The centimeter long pteropod flapping briskly at 5Hz escapes attention with its size and speed. Modeling the clap-and-fling motion of the sea butterfly at a larger scale allows the benefits and uniqueness …

Development Of A Plant Growth And Health Monitoring System Using Imaging And Sensor Array Information For Cubesat Applications, Kat-Kim Phan

USF Tampa Graduate Theses and Dissertations

Space exploration has been a topic of interest in the scientific community, such as the planned missions to Mars. To accomplish this would require being able to provide astronauts with a steady supply of food beyond freeze-dried foods leading to the need to grow food in space. Although this is a topic still being investigated, the CubeSat platform opens the possibility of carrying out studies on plant growth under more strenuous space conditions unlike that in the International Space Station (ISS). Developing a plant-focused mission for a CubeSat, though, entails being able to develop a system to sustain plant life …

Temporospatial Deep Learning Strategies For Prediction Of Disease Progression In Radiology, John D. Mayfield

USF Tampa Graduate Theses and Dissertations

While the fields of machine learning and medicine are deeply rooted in axiomatic scientific principles, there is an element of art that makes the practice imperfect, yet innately human. As the two fields have seen the greatest overlap in their collective history, there remains a chasm between them in terms of practical translation for the patients who desire and deserve personalized medicine. As presented in this dissertation, I and my collaborators have contributed to the groundwork for future exploration of predicting disease progression by identifying signals within sequential medical imaging to provide a temporospatial relationship upon which we can make …