Digital Commons Network^™

An Analysis And Ontology Of Teaching Methods In Cybersecurity Education, Sarah Buckley

LSU Master's Theses

The growing cybersecurity workforce gap underscores the urgent need to address deficiencies in cybersecurity education: the current education system is not producing competent cybersecurity professionals, and current efforts are not informing the non-technical general public of basic cybersecurity practices. We argue that this gap is compounded by a fundamental disconnect between cybersecurity education literature and established education theory. Our research addresses this issue by examining the alignment of cybersecurity education literature concerning educational methods and tools with education literature.

In our research, we endeavor to bridge this gap by critically analyzing the alignment of cybersecurity education literature with education theory. …

Sensor Analytics For Subsea Pipeline And Cable Inspection: A Review, Connor R. Vincent

LSU Master's Theses

Submarine pipelines and cables are vital for transmitting physical and digital resources across bodies of water, necessitating regular inspection to assess maintenance needs. The safety of subsea pipelines and cables is paramount for sustaining industries such as telecommunications, power transmission, water supply, waste management, and oil and gas. Incidents like those involving the Nord Stream subsea pipeline and the SEA-ME-WE 4 subsea communications cable exemplify the severe economic and environmental consequences of damage to these critical infrastructures. Existing inspection methods often fail to meet accuracy requirements, emphasizing the need for advancements in inspection technologies. This comprehensive survey covers the sensors …

Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken

LSU Master's Theses

Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …

Evaluating Attack Surface Management In An Industrial Control System (Ics) Environment: Leveraging A Recon Ftw For Threat Classification And Incident Response, Nathalia De Sa Soares

LSU Master's Theses

Protecting Industrial Control Systems (ICS) from cyber threats is paramount to
ensure the reliability and security of critical infrastructure. Organizations must proactively identify vulnerabilities and strengthen their incident response capabilities as attack vectors evolve. This research explores implementing an Attack Surface Management (ASM) approach, utilizing Recon FTW, to assess an operating ICS environment’s security posture comprehensively.
The primary objective of this research is to develop a tool for performing recon-
naissance in an ICS environment with a non-intrusive approach, enabling the realistic simulation of potential threat scenarios and the identification of critical areas requiring immediate attention and remediation. We aim …

Autonomous Shipwreck Detection & Mapping, William Ard

LSU Master's Theses

This thesis presents the development and testing of Bruce, a low-cost hybrid Remote Operated Vehicle (ROV) / Autonomous Underwater Vehicle (AUV) system for the optical survey of marine archaeological sites, as well as a novel sonar image augmentation strategy for semantic segmentation of shipwrecks. This approach takes side-scan sonar and bathymetry data collected using an EdgeTech 2205 AUV sensor integrated with an Harris Iver3, and generates augmented image data to be used for the semantic segmentation of shipwrecks. It is shown that, due to the feature enhancement capabilities of the proposed shipwreck detection strategy, correctly identified areas have a 15% …

Finding Forensic Evidence In The Operating System's Graphical User Interface, Edward X. Wilson Mr.

LSU Master's Theses

A branch of cyber security known as memory forensics focuses on extracting meaningful evidence from system memory. This analysis is often referred to as volatile memory analysis, and is generally performed on memory captures acquired from target systems. Inside of a memory capture is the complete state of a system under investigation, including the contents of currently running as well as previously executed applications. Analysis of this data can reveal a significant amount of activity that occurred on a system since the last reboot. For this research, the Windows operating system is targeted. In particular, the graphical user interface component …

Enabling The Human Perception Of A Working Camera In Web Conferences Via Its Movement, Anish Shrestha

LSU Master's Theses

In recent years, video conferencing has seen a significant increase in its usage due to the COVID-19 pandemic. When casting user’s video to other participants, the videoconference applications (e.g. Zoom, FaceTime, Skype, etc.) mainly leverage 1) webcam’s LED-light indicator, 2) user’s video feedback in the software and 3) the software’s video on/off icons to remind the user whether the camera is being used. However, these methods all impose the responsibility on the user itself to check the camera status, and there have been numerous cases reported when users expose their privacy inadvertently due to not realizing that their camera is …

2-Dimensional String Problems: Data Structures And Quantum Algorithms, Dhrumilkumar Patel

LSU Master's Theses

The field of stringology studies algorithms and data structures used for processing strings efficiently. The goal of this thesis is to investigate 2-dimensional (2D) variants of some fundamental string problems, including \textit{Exact Pattern Matching} and \textit{Longest Common Substring}.

In the 2D pattern matching problem, we are given a matrix $\M[1\dd n,1\dd n]$ that consists of $N = n \times n$ symbols drawn from an alphabet $\Sigma$ of size $\sigma$. The query consists of a $ m \times m$ square matrix $\PP[1\dd m, 1\dd m]$ drawn from the same alphabet, and the task is to find all the locations of $\PP$ …

Control And Planning For Mobile Manipulators Used In Large Scale Manufacturing Processes, Joshua T. Nguyen

LSU Master's Theses

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for …

Neutron Interferometry Using A Single Modulated Phase Grating, Ivan J. Hidrovo Giler

LSU Master's Theses

Neutron grating interferometry provides information on phase and small-angle scatter in addition to attenuation. Previously, phase grating moiré interferometers (PGMI) with two or three phase gratings have been developed. These phase-grating systems use the moiré far-field technique to avoid the need for high-aspect absorption gratings used in Talbot-Lau interferometers (TLI) which reduce the neutron flux reaching the detector. We demonstrate through simulations a novel phase grating interferometer system for cold neutrons that requires a single modulated phase grating (MPG) for phase-contrast imaging, as opposed to the two or three phase gratings in previously employed PGMI systems. We compare the MPG …

Improving Kernel Artifact Extraction In Linux Memory Samples Using The Slub Allocator, Daniel A. Donze

LSU Master's Theses

Memory forensics allows an investigator to analyze the volatile memory (RAM) of a computer, providing a view into the system state of the machine as it was running. Examples of items found in memory samples that are of interest to investigators are kernel data structures which can represent processes, files, and sockets. The SLUB allocator is the default small-request memory allocator for modern Linux systems. SLUB allocates “slabs”, which are contiguous sections of pre-allocated memory that are used to efficiently service allocation requests. The predecessor to SLUB, the SLAB allocator, tracked every slab it allocated, allowing extraction of allocated slabs …

Malware And Memory Forensics On M1 Macs, Charles E. Glass

LSU Master's Theses

As malware continues to evolve, infection mechanisms that can only be seen in memory are increasingly commonplace. These techniques evade traditional forensic analysis, requiring the use of memory forensics. Memory forensics allows for the recovery of historical data created by running malware, including information that it tries to hide. Memory analysis capabilities have lagged behind on Apple's new M1 architecture while the number of malicious programs only grows. To make matters worse, Apple has developed Rosetta 2, the translation layer for running x86_64 binaries on an M1 Mac. As a result, all malware compiled for Intel Macs is theoretically functional …

Improving Memory Forensics Capabilities On Apple M1 Computers, Raphaela Santos Mettig Rocha

LSU Master's Theses

Malware threats are rapidly evolving to use more sophisticated attacks. By abusing rich application APIs such as Objective-C’s, they are able to gather information about user activity, launch background processes without the user’s knowledge as well as perform other malicious activities. In some cases, memory forensics is the only way to recover artifacts related to this malicious activity, as is the case with memory-only execution. The introduction of the Rosetta 2 on the Apple M1 introduces a completely new attack surface by allowing binaries of both Intel x86 64 and ARM64 architecture to run in userland. For this reason it …

Rethinking The Design Of Online Professor Reputation Systems, Haley Tatum

LSU Master's Theses

Online Professor Reputation (OPR) systems, such as RateMyProfessors.com (RMP), are frequently used by college students to post and access peer evaluations of their pro- fessors. However, recent evidence has shown that these platforms suffer from major bias problems. Failing to address bias in online professor ratings not only leads to negative expectations and experiences in class, but also poor performance on exams. To address these concerns, in this thesis, we study bias in OPR systems from a software design point of view. At the first phase of our analysis, we conduct a systematic literature review of 23 interdisciplinary studies on …

Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin

LSU Master's Theses

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, …

Memory Forensics Comparison Of Apple M1 And Intel Architecture Using Volatility Framework, Joshua Duke

LSU Master's Theses

Memory forensics allows an investigator to get a full picture of what is occurring on-device at the time that a memory sample is captured and is frequently used to detect and analyze malware. Malicious attacks have evolved from living on disk to having persistence mechanisms in the volatile memory (RAM) of a device and the information that is captured in memory samples contains crucial information for full forensic analysis by cybersecurity professionals. Recently, Apple unveiled computers containing a custom designed system on a chip (SoC) called the M1 that is based on ARM architecture. Our research focused on the differences …

Evaluation Of Algorithms For Randomizing Key Item Locations In Game Worlds, Caleb Johnson

LSU Master's Theses

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid.

In order to accomplish this shuffling in such a way that the player is able to …

Development Of Reduced Order Models Using Reservoir Simulation And Physics Informed Machine Learning Techniques, Mark V. Behl Jr

LSU Master's Theses

Reservoir simulation is the industry standard for prediction and characterization of processes in the subsurface. However, simulation is computationally expensive and time consuming. This study explores reduced order models (ROMs) as an appropriate alternative. ROMs that use neural networks effectively capture nonlinear dependencies, and only require available operational data as inputs. Neural networks are a black box and difficult to interpret, however. Physics informed neural networks (PINNs) provide a potential solution to these shortcomings, but have not yet been applied extensively in petroleum engineering.

A mature black-oil simulation model from Volve public data release was used to generate training data …

Automated Extraction Of Network Activity From Memory Resident Code, Austin Nicholas Sellers

LSU Master's Theses

Advancements in malware development, including the use of file-less and memory-only payloads, have led to a significant interest in the use of volatile memory analysis by digital forensics practitioners. Memory analysis can uncover a wealth of information not available via traditional analysis, such as the discovery of injected code, hooked APIs, and more. Unfortunately, the process of analyzing such malicious code is largely left to analysts who must manually reverse engineer the code to discover its intent. This task is not only slow and error-prone, but is also generally left only to senior-level analysts to perform, given that significant reverse …

Improving Ocr Accuracy Of Damaged Pictures With Generative Adversarial Networks, Pu Du

LSU Master's Theses

In this thesis, we focus on resolving the inpainting problem and improving Optical Character Recognition (OCR) accuracy of damaged text images at character level. We present a Generative Adversarial Network (GAN)-based model conditioned on class labels for image inpainting. This model is a deep convolutional neural network with encoder-decoder style architecture which can process images with holes at random locations. Experiments on the character images dataset demonstrate that our proposed model generates promising inpainting results and significantly improve OCR accuracy by reconstructing missing parts of damaged character images.

High Performance Fuzz Testing Of Memory Forensics Frameworks, Arian Dokht Shahmirza

LSU Master's Theses

The analysis of the volatile memory (RAM) of a computer system, known as memory forensics, is a critical component of modern digital forensics investigations. Since the evidence provided by memory forensics is vital, it is necessary for there to be automated solutions that implement the analysis. Volatility is the most widely used memory forensics framework and also contains the most functionality of all tools publicly available. Volatility, as well as all other memory forensics frameworks, are extremely complex software systems as they must parse a substantial number of in-memory data structures and their associated values. Given the reliance on memory …

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

Mosquito Image Classification Using Convolutional Neural Networks, Sumanth Vissamsetty

LSU Master's Theses

Human life has always been affected by insects, especially mosquitoes, since it's early beginnings. This pesky insect acts as a vector that transmit pathogens through feeding on our blood, spreading life-threatening diseases like Zika Virus, Malaria, Dengue fever, Chikungunya and more. It is important to prevent these mosquitoes from harming humans and one way to do so is to control the mosquito population, or mosquito abatement as it is commonly known. It is important to note that not all mosquitoes are the same and each of them live, reproduce and attack in their own unique way. Hence it is crucial …

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

Predicting River Stage Using Recurrent Neural Networks, Eric Rohli

LSU Master's Theses

River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …

Distributed Iterative Graph Processing Using Nosql With Data Locality, Ayam Pokhrel

LSU Master's Theses

A tremendous amount of data is generated every day from a wide range of sources such as social networks, sensors, and application logs. Among them, graph data is one type that represents valuable relationships between various entities. Analytics of large graphs has become an essential part of business processes and scientific studies because it leads to deep and meaningful insights into the related domain based on the connections between various entities. However, the optimal processing of large-scale iterative graph computations is very challenging due to the issues like fault tolerance, high memory requirement, parallelization, and scalability. Most of the contemporary …

Using Github In Large Software Engineering Classes: An Exploratory Case Study, Miroslav Tushev

LSU Master's Theses

GitHub has been recently used in Software Engineering (SE) classes to facilitate col- laboration in student team projects. The underlying tenet is that the technical and social feature of GitHub can help students to communicate and collaborate more effectively as a team as well as help teachers to evaluate individual student contribution more objectively. To shed more light on this, in this case study, we explore the benefits and drawbacks of using GitHub in SE classes. Our study is conducted in a software engineering class of 91 students divided into 18 teams. Our research method includes an entry and an …

A Study On User Demographic Inference Via Ratings In Recommender Systems, Changbin Li

LSU Master's Theses

Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer …

Interactive Web-Based Visualization Of Atomic Position-Time Series Data, Simron Thapa

LSU Master's Theses

Extracting and interpreting the information contained in large sets of time-varying three dimensional positional data for the constituent atoms of simulated material system is a challenging task. This thesis work reports our initial implementation of a web-based visualization system and its use-case study. The system allows the users to perform the desired visualization task on a web browser for the position-time series data extracted from the local or remote hosts. It involves a pre-processing step for data reduction, which involves skipping uninteresting parts of the data uniformly (at full atomic configuration level) or non-uniformly (at atomic species level or individual …

Analyzing User Comments On Youtube Coding Tutorial Videos, Elizabeth Heidi Poche

LSU Master's Theses

Video coding tutorials enable expert and novice programmers to visually observe real developers write, debug, and execute code. Previous research in this domain has focused on helping programmers find relevant content in coding tutorial videos as well as understanding the motivation and needs of content creators. In this thesis, we focus on the link connecting programmers creating coding videos with their audience. More specifically, we analyze user comments on YouTube coding tutorial videos. Our main objective is to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality …