Other Computer Engineering Commons^™

Time-Robust Evaluation For Multi-Dataset Intrusion Detection Reveals Temporal Shortcuts And Strong Baselines, Kyle A. Mccleary

LSU Master's Theses

Pooled multi-dataset benchmarks are an attractive way to evaluate intrusion detection systems (IDS) across heterogeneous public corpora, but they can quietly reward shortcut features tied to capture schedules and dataset identity. This work introduces TRACER, an auditable benchmark specification that standardizes seven public IDS corpora into a shared transaction-window prediction unit and a shared label ontology, enabling controlled comparisons between compact sequence backbones and strong tabular baselines under matched splits, training budgets, and scoring rules.

Under this protocol, absolute clock time is a strong shortcut under pooled random splits. Enforcing time-robust controls (timestamp rebasing, circular shifts, and schedule-token masking) reduces …

Chplx: The Hpx Solution For Bridging Chapel And C++, Shreyas Swanand Atre

LSU Master's Theses

Historically, domain scientists faced steep learning curves due to low-level programming models and fragmented tooling. Between 2003 and 2008, Cray, now part of HPE, introduced the Chapel language as part of DARPA’s High Productivity Computing Systems (HPCS) program. Today, Chapel remains under active development and is used across research and production projects. In parallel, the STE||AR Group has advanced C++-based parallel programming through HPX, a standards-conforming runtime that provides lightweight tasking, futures, and distributed execution while abstracting much of the algorithmic “heavy lifting.” Yet for many domain scientists, C++ presents a steeper learning curve than Chapel. To close this complexity …

Adaptive Multi-Agent Reinforcement Learning For Electric Vehicle Charging Optimization Under Dynamic Traffic Conditions, Shaghayegh Rabbanian

LSU Master's Theses

Electric vehicle (EV) charging optimization is a critical challenge in sustainable transportation. This study focuses on three fundamental questions: (1) when is the best time to charge an EV, (2) where is the optimal charging location, and (3) how should charging be planned considering navigation and routing decisions. Our primary objective is to determine the optimal time and location for EV charging while accounting for key factors such as real-time traffic conditions, spatial distribution of charging stations, and EV-specific attributes such as state of charge (SOC), driving range, and efficiency. To develop a robust and adaptive EV charging recommendation system, …

From Devices To The Cloud: Digital Forensics In The Changing Social Media Landscape, Joseph Brown

LSU Master's Theses

This thesis presents a comprehensive digital forensic analysis of emerging and alternative social media platforms, including Truth Social, Threads, Bluesky, Nextdoor, and Neighbors. These platforms, which range from politically aligned alt-tech networks to hyperlocal neighborhood apps, present unique forensic challenges and security vulnerabilities. Across all case studies, established forensic techniques were applied using a hybrid methodology combining mobile device analysis, network traffic monitoring, and API interrogation. Findings include the discovery of plaintext credentials, session tokens, and other sensitive artifacts, particularly in platforms with weaker security postures such as Truth Social, Bluesky, Nextdoor, and Neighbors. Threads, by contrast, demonstrated greater resilience …

Cyber Attacks Against Industrial Control Systems, Adam Kardorff

LSU Master's Theses

Industrial Control Systems (ICS) are the foundation of our critical infrastructure, and allow for the manufacturing of the products we need. These systems monitor and control power plants, water treatment plants, manufacturing plants, and much more. The security of these systems is crucial to our everyday lives and to the safety of those working with ICS. In this thesis we examined how an attacker can take control of these systems using a power plant simulator in the Applied Cybersecurity Lab at LSU. Running experiments on a live environment can be costly and dangerous, so using a simulated environment is the …

An Investigation On The Resilience Of Long Short-Term Memory Deep Neural Networks, Christopher Vasquez

LSU Master's Theses

In a world of continuously advancing technology, the reliance on these technologies continues to increase. Recently, transformer networks [22] have been implemented through various projects such as ChatGPT. These networks are extremely computationally demanding and require cutting-edge hardware to explore. However, with the growing increase and popularity of these neural networks, a question of reliability and resilience comes about, especially as the dependency and research on these networks grow. Given the computational demand of transformer networks, we investigate the resilience of the weights and biases of the predecessor of these networks, i.e. the Long Short-Term (LSTM) neural network, through four …

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 …

Device Free Indoor Localization Of Human Target Using Wifi Fingerprinting, Prasanga Neupane

LSU Master's Theses

Indoor localization of human objects has many important applications nowadays. Proposed here is a new device free approach where all the transceiver devices are fixed in an indoor environment so that the human target doesn't need to carry any transceiver device with them. This work proposes radio-frequency fingerprinting for the localization of human targets which makes this even more convenient as radio-frequency wireless signals can be easily acquired using an existing wireless network in an indoor environment. This work explores different avenues for optimal and effective placement of transmitter devices for better localization. In this work, an experimental environment is …

Towards Verifying Smartphone Users Via Gripping Hand Image Classification, Kaitlyn M. Madden

LSU Master's Theses

Smartphones continue to proliferate throughout our daily lives, not only in sheer quantity but also their ever-growing list of uses. They are no longer just for communication and the occasional phone game. Smartphones can be used to open garage doors, transfer money, see who is at your front door, and much, much more. With this increased dependence and use, smartphone security is critical. In this paper we propose a system to verify a user’s identity by applying a convolutional neural network (CNN) model to an image of the user’s hand while holding their device. This model aims to address situations …

An Approach To Counting Vehicles From Pre-Recorded Video Using Computer Algorithms, Mishuk Majumder

LSU Master's Theses

One of the fundamental sources of data for traffic analysis is vehicle counts, which can be conducted either by the traditional manual method or by automated means. Different agencies have guidelines for manual counting, but they are typically prepared for particular conditions. In the case of automated counting, different methods have been applied, but You Only Look Once (YOLO), a recently developed object detection model, presents new potential in automated vehicle counting. The first objective of this study was to formulate general guidelines for manual counting based on experience gained in the field. Another goal of this study was to …

Rfid Item-Level Tagging In A Grocery Store Environment, Brian Truman

LSU Master's Theses

The purpose of this research was to investigate how effective item-level Radio Frequency Identification (RFID) tagging would be using current RFID technology as a replacement for barcodes in a supermarket/grocery store environment.

To accomplish this, an experiment was be performed that utilized commercially available RFID technology. Passive Ultra High Frequency (UHF) RFID Tags were affixed to various grocery store items of different material categories (Food, Metal, Plastic, Liquid, and Glass), and placed in a metal shopping cart. Eight (8) antenna arrangements were created, comprised of different combinations of four (4) antennas in different locations around the cart.

The experiment was …

Effective Fuzzing Framework For The Sleuthkit Tools, Shravya Paruchuri

LSU Master's Theses

The fields of digital forensics and incident response have seen significant growth over the last decade due to the increasing threats faced by organizations and the continued reliance on digital platforms and devices by criminals. In the past, digital investigations were performed manually by expert investigators, but this approach has become no longer viable given the amount of data that must be processed compared to the relatively small number of trained investigators. These resource constraints have led to the development and reliance on automated processing and analysis systems for digital evidence. In this paper, we present our effort to develop …

Thermal-Kinect Fusion Scanning System For Bodyshape Inpainting And Estimation Under Clothing, Sirazum Munira Tisha

LSU Master's Theses

In today's interactive world 3D body scanning is necessary in the field of making virtual avatar, apparel industry, physical health assessment and so on. 3D scanners that are used in this process are very costly and also requires subject to be nearly naked or wear a special tight fitting cloths. A cost effective 3D body scanning system which can estimate body parameters under clothing will be the best solution in this regard. In our experiment we build such a body scanning system by fusing Kinect depth sensor and a Thermal camera. Kinect can sense the depth of the subject and …

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 …

Evaluating Classifiers' Optimal Performances Over A Range Of Misclassification Costs By Using Cost-Sensitive Classification, Ramy Al-Saffar

LSU Master's Theses

We believe that using the classification accuracy is not enough to evaluate the performances of classification algorithms. It can be misleading due to overlooking an important element which is the cost if classification is inaccurate. Furthermore, the Receiver Operational Characteristic (ROC) is one of the most popular graphs used to evaluate classifiers performances. However, one of the biggest ROC’s shortcomings is the assumption of equal costs for all misclassified data. Therefore, our goal is to reduce the total cost of decision making by selecting the classifier that has the least total misclassification cost. Nevertheless, the exact misclassification cost is usually …

Data-Driven Abstraction, Vivian Mankau Ho

LSU Master's Theses

Given a program analysis problem that consists of a program and a property of interest, we use a data-driven approach to automatically construct a sequence of abstractions that approach an ideal abstraction suitable for solving that problem. This process begins with an infinite concrete domain that maps to a finite abstract domain defined by statistical procedures resulting in a clustering mixture model. Given a set of properties expressed as formulas in a restricted and bounded variant of CTL, we can test the success of the abstraction with respect to a predefined performance level. In addition, we can perform iterative abstraction-refinement …