Engineering Commons^™

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.

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera

USF Tampa Graduate Theses and Dissertations

Quantification of the true number of stained cells in specific brain regions is an important metric in many fields of biomedical research involving cell degeneration, cytotoxicology, cellular inflammation, and drug development for a wide range of neurological disorders and mental illnesses. Unbiased stereology is the current state-of-the-art method for collecting the cell count data from tissue sections. These studies require trained experts to manually focus through a z-stack of microscopy images and count (click) on a hundred or more cells per case, making this approach time consuming (~1 hour per case) and prone to human error (i.e., inter-rater variability). Thus, …

Analyzing Multi-Robot Leader-Follower Formations In Obstacle-Laden Environments, Zachary J. Hinnen

USF Tampa Graduate Theses and Dissertations

Observations in biological formation from nature likes flocks of birds, herds of mammals and packs of wolves have inspired the innovation of robotic architectures. This thesis presents an approach that aims to use robotic systems to mimic leader-follower behaviors in the navigation and formation of sparse and dense environments. The goal of this work is to extend and further analyze the original work of Weitzenfeld et al [3] to evaluate new swarm and pack based multi-robot architectures with the inclusion of obstacle avoidance and variations in group formations. The multiple robot architecture is based off a wolf pack with a …

Cyber-Physical Multi-Robot Systems In A Smart Factory: A Networked Ai Agents Approach, Zixiang Nie

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization—all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.

However, the implementation of MRS introduces new sets of challenges, including …

Secure Lightweight Cryptographic Hardware Constructions For Deeply Embedded Systems, Jasmin Kaur

USF Tampa Graduate Theses and Dissertations

Lightweight cryptography plays a vital role in securing resource-constrained deeply-embedded systems such as implantable and wearable medical devices, smart fabrics, smart homes, radio frequency identification tags, sensor networks, and privacy-constrained usage models. The National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight cryptography, a relatively-long multi-year effort, which eventually concluded in February 2023. Side-channel attacks (SCAs) exploit the vulnerabilities of a system by observing and analyzing side-channel information leakages. Fault analysis attacks are a type of active SCAs, where an intelligent adversary injects bit/byte faults into the implementation of a cryptographic cipher to recover the secret …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Robot Learning To Pour Solid Objects Accurately, Juan Wilches, Yu Sun

36th Florida Conference on Recent Advances in Robotics

Pouring is an efficient way to transfer objects from
one container to another. This abstract summarizes a method
to accurately pour solid objects, such as ice cubes. It leverages
visual and proprioceptive feedback together with contextual
information to control the forward and backward rotation of the
pouring container. These feedback signals are fed to a recurrent
neural network that produces the control signal. The proposed
approach can achieve a human-like pouring accuracy in both a
simulation and a real setup.

Multi-Object Grasping -- Stochastic Grasping From A Pile, Tianze Chen, Adheesh Shenoy, Yu Sun

36th Florida Conference on Recent Advances in Robotics

Grasping multiple objects at once from a pile is common for humans. It makes us efficient in pick and transfer tasks. It is essential for a robot to gain multi-object grasping capability (MOG). This paper defines the multi-object grasping problem and introduces several novel multi-object grasping techniques. These techniques include probability-based pre-grasp potential calculation, a stochastic flexing/extending routine, obtaining end-grasp types, and estimating the number of objects in a grasp. It also proposes a new stochastic grasping strategy for grasping a desired number of objects.

Live Audiovisual Remote Assistance System (Laras) For Person With Visual Impairments, Zachary Frey, Nghia Vo, Varaha Maithreya, Tais Mota, Urvish Trivedi, Redwan Alqasemi, Rajiv Dubey

36th Florida Conference on Recent Advances in Robotics

According to "The World Report on Vision" by World Health Organization (WHO) [1], there are more than 2.2 billion people who have near or distant vision Impairments, out of which 36 million people are classified as entirely blind. This report also emphasizes the importance of social and communal support in enabling individuals with vision impairments to integrate into society and reach their full potential. While performing daily activities and navigating the environment, people with visual impairments (PVIs) often require direct or synchronous assistance [2]. Consequently, there is a growing need for automated solutions to assist in this regard. However, existing …

Assistive Robotic Platform For Non‐Urgent Household Tasks: A New Design, Amanda Serger, Normandy Tanguilan, Hakki Erhan Sevil

36th Florida Conference on Recent Advances in Robotics

Humans overcome minor household inconveniences daily without fully recognizing how challenging these tasks could be for individuals such as elderly people or people with disabilities. Those people often times struggle to complete tasks, for instance opening a door or reaching for an item, leading them to rely on caregivers for help. During the COVID-19 pandemic, this caregiver support becomes an unsafe and unreliable solution that can result in a greater risk, thus the need for another solution arises: robotic technology. Recent developments in the robotics field have paved the way for this research, aiming to design a home assistance robot …

Reinforcement Learning And Place Cell Replay In Spatial Navigation, Chance Hamilton, Pablo Scleidorovich Phd, Alfredo Weitzenfeld Phd

36th Florida Conference on Recent Advances in Robotics

In the last decade, studies have demonstrated that hippocampal place cells influence rats’ navigational learning ability. Moreover, researchers have observed that place cell sequences associated with routes leading to a reward are reactivated during rest periods. This phenomenon is known as Hippocampal Replay, which is thought to aid navigational learning and memory consolidation. These findings in neuroscience have inspired new robot navigation models that emulate the learning process of mammals. This study presents a novel model that encodes path information using place cell connections formed during online navigation. Our model employs these connections to generate sequences of
state-action pairs to …

Biologically Inspired Multi-Robot System Based On Wolf Hunting Behavior, Zachary Hinnen, Chance Hamilton, Alfredo Weitzenfeld

36th Florida Conference on Recent Advances in Robotics

Studies involving the group predator behavior of wolves have inspired multiple robotic architectures to mimic these biological behaviors in their designs and research. In this work, we aim to use robotic systems to mimic wolf packs' single and group behavior. This work aims to extend the original research by Weitzenfeld et al [7] and evaluate under a new multi-robot robot system architecture. The multiple robot architecture includes a 'Prey' pursued by a wolf pack consisting of an 'Alpha' and 'Beta' robotic group. The Alpha Wolf' will be the group leader, searching and tracking the 'Prey.' At the same time, the …

Programming By Demonstration Using Learning Based Approach: A Mini Review, Atul Acharya, Rajiv Dubey, Redwan Alqasemi

36th Florida Conference on Recent Advances in Robotics

Wheelchair-mounted robotic arms are used in rehabilitation robotics to help physically impaired people perform ADL (Activity of daily living) tasks. However, the dexterity of manipulation tasks makes the teleoperation of the robotic arm challenging for the user, as it is difficult to control all degrees of freedom with a handheld joystick or a screen touch device. PbD (Programming by demonstration) allows the user to demonstrate the desired behavior and enables the system to learn from the demonstrations and adapt to a new environment. This learned model can perform a new set of actions in a new environment. Learning from a …

Security-Enhanced Serial Communications, John White, Alexander Beall, Joseph Maurio, Dane Fichter, Dr. Matthew Davis, Dr. Zachary Birnbaum

Military Cyber Affairs

Industrial Control Systems (ICS) are widely used by critical infrastructure and are ubiquitous in numerous industries including telecommunications, petrochemical, and manufacturing. ICS are at a high risk of cyber attack given their internet accessibility, inherent lack of security, deployment timelines, and criticality. A unique challenge in ICS security is the prevalence of serial communication buses and other non-TCP/IP communications protocols. The communication protocols used within serial buses often lack authentication and integrity protections, leaving them vulnerable to spoofing and replay attacks. The bandwidth constraints and prevalence of legacy hardware in these systems prevent the use of modern message authentication and …

Enhancing The Battleverse: The People’S Liberation Army’S Digital Twin Strategy, Joshua Baughman

Military Cyber Affairs

No abstract provided.

Operationalizing Deterrence By Denial In The Cyber Domain, Gentry Lane

Military Cyber Affairs

No abstract provided.

What Senior U.S. Leaders Say We Should Know About Cyber, Dr. Joseph H. Schafer

Military Cyber Affairs

On April 6, 2023, the Atlantic Council’s Cyber Statecraft Initiative hosted a panel discussion on the new National Cybersecurity Strategy. The panel featured four senior officials from the Office of the National Cyber Director (ONCD), the Department of State (DoS), the Department of Justice (DoJ), and the Department of Homeland Security (DHS). The author attended and asked each official to identify the most important elements that policymakers and strategists must understand about cyber. This article highlights historical and recent struggles to express cyber policy, the responses from these officials, and the author’s ongoing research to improve national security cyber policy.

A Human-In-The-Loop Robot Grasping System With Grasp Quality Refinement, Tian Tan

USF Tampa Graduate Theses and Dissertations

The goal of this dissertation is to develop a grasping system for assistive robots that can help people with disabilities and the elderly to perform tasks of daily living. In developing this robot grasping system, we maximize its reliability, accuracy, and autonomy. High reliability and accuracy are required for robots to perform tasks around human users and to safely interact with objects that might be fragile or have contents that could spill. High autonomy is desired as users with disabilities are usually not dexterous enough to directly operate the robot. In this dissertation, a human-in-the-loop (HitL) robot grasping system is …

Process Automation And Robotics Engineering For Industrial Processing Systems, Drake Stimpson

USF Tampa Graduate Theses and Dissertations

Automation in industrial systems applications has emerged as the fundamental solution for improving quality, production rate, and efficiency of a process. Much of the recent popularity surrounding the transition of processes from manually operated tasks to automated systems can be attributed to the concept of Industry 4.0, which outlines the fundamental guidelines for integrating cyber-physical systems into industrial processes. Due to rapid advancement of technology in robotics and automation as well as the increase in accessibility of resources to this technology, the capability to develop automated systems has become feasible for small-scale enterprise. This work presents a two-part initiative to …

Towards More Task-Generalized And Explainable Ai Through Psychometrics, Alec Braynen

USF Tampa Graduate Theses and Dissertations

In this work, we propose that adopting the methods, principles, and guidelines of the field of psychometrics can help the Artificial Intelligence (AI) community to build more task-generalizable and explainable AI. Three arguments are presented and explored. These arguments are that psychometrics can help by providing 1) a framework for formulating better datasets, 2) psychometric AI data that can lead to models of generalization in AI, and 3) explainable AI through more informative evaluations.

A review of psychometrics and psychological generalization is performed, along with an overview of evaluation, generalization, and explainability in AI. Various ideas are presented throughout for …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

Preventing Variadic Function Attacks Through Argument Width Counting, Brennan Ward

USF Tampa Graduate Theses and Dissertations

Format String attacks, first noted in June 2000 [1], are a type of attack in which anadversary has control of the string argument (the format string) passed to a string format function (such as printf). Such control allows the attacker to read and write arbitrary program memory. To prevent these attacks, various methodologies have been proposed, each with their own costs and benefits. I present a novel solution to this problem through argument width counting, ensuring that such format functions cannot access stack memory beyond the space where arguments were placed. Additionally, I show how this approach can be expanded …

Adaptive Multi-Scale Place Cell Representations And Replay For Spatial Navigation And Learning In Autonomous Robots, Pablo Scleidorovich

USF Tampa Graduate Theses and Dissertations

Place cells are one of the most widely studied neurons thought to play a vital role in spatial cognition. Extensive studies show that their activity in the rodent hippocampus is highly correlated with the animal’s spatial location, forming “place fields” of smaller sizes near the dorsal pole and larger sizes near the ventral pole. Despite advances, it is yet unclear how this multi-scale representation enables navigation in complex environments.

In this dissertation, we analyze the place cell representation from a computational point of view, evaluating how multi-scale place fields impact navigation in large and cluttered environments. The objectives are to …