Digital Communications and Networking Commons^™

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

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 …

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 …

Learning Proximal Operators With Gaussian Process And Adaptive Quantization In Distributed Optimization, Aldo Duarte Vera Tudela

LSU Doctoral Dissertations

In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a further decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Integrating Gaussian processes (GP) as a learning component to the Alternating Direction Method of Multipliers (ADMM) has proven effective in learning each agent's local proximal operator to …

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 …

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 …

Monitoring Of Remote Hydrocarbon Wells Using Azure Internet Of Things, Derek W. Staal

LSU Master's Theses

Remote monitoring of hydrocarbon wells is a tedious and meticulously thought out task performed to create a cyber-physical bridge between the asset and the owner. There are many systems and techniques on the market that offer this solution but due to their lack of interoperability and/or decentralized architecture they begin to fall apart when remote assets become farther away from the client. This results in extreme latency and thus poor decision making. Microsoft's Azure IoT Edge was the focus of this writing. Coupled with off-the-shelf hardware, Azure's IoT Edge services were integrated with an existing unit simulating a remote hydrocarbon …

Security Analysis Of The Anonabox Pro, Devi Sowjanya Vempati

LSU Master's Theses

The security posture of critical network appliances, such as routers, switches, and firewalls, is crucial as these devices provide the first line of defense against malicious actors. As there is typically no endpoint security software, such as anti-virus available for these devices, it is crucial that vendors release timely updates for vulnerabilities and that customers apply them quickly. The research effort documented in this thesis describes a developed methodology for triaging known vulnerabilities in network devices, assessing the security posture of device vendors, and locating likely weak points in released firmware images. To highlight this methodology, the Anonabox security appliance …

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 …

Secure And Efficient Bft Consensus For Blockchains, Mohammad Mussadiq Jalalzai

LSU Doctoral Dissertations

Blockchains are simple data structures, containing transactions organized into blocks, in which each block points to a previous block using its hash. Thus, by following the chain, we can follow the history of transactions. Blocks are added to the chain through a consensus mechanism. Byzantine Fault Tolerant (BFT) consensus protocols that were designed before blockchains were introduced are usually considered appropriate for use in small scale networks of size 10-20 replicas. Blockchains have changed this trend as the blockchain networks usually require a larger number of replicas and classic BFT protocols cannot provide acceptable performance in large networks. One of …

Bipartite Quantum Interactions: Entangling And Information Processing Abilities, Siddhartha Das

LSU Doctoral Dissertations

The aim of this thesis is to advance the theory behind quantum information processing tasks, by deriving fundamental limits on bipartite quantum interactions and dynamics. A bipartite quantum interaction corresponds to an underlying Hamiltonian that governs the physical transformation of a two-body open quantum system. Under such an interaction, the physical transformation of a bipartite quantum system is considered in the presence of a bath, which may be inaccessible to an observer. The goal is to determine entangling abilities of such arbitrary bipartite quantum interactions. Doing so provides fundamental limitations on information processing tasks, including entanglement distillation and secret key …

A Study Of Very Short Intermittent Ddos Attacks On The Performance Of Web Services In Clouds, Huasong Shan

LSU Doctoral Dissertations

Distributed Denial-of-Service (DDoS) attacks for web applications such as e-commerce are increasing in size, scale, and frequency. The emerging elastic cloud computing cannot defend against ever-evolving new types of DDoS attacks, since they exploit various newly discovered network or system vulnerabilities even in the cloud platform, bypassing not only the state-of-the-art defense mechanisms but also the elasticity mechanisms of cloud computing.

In this dissertation, we focus on a new type of low-volume DDoS attack, Very Short Intermittent DDoS Attacks, which can hurt the performance of web applications deployed in the cloud via transiently saturating the critical bottleneck resource of the …

A Study Of Application-Awareness In Software-Defined Data Center Networks, Chui-Hui Chiu

LSU Doctoral Dissertations

A data center (DC) has been a fundamental infrastructure for academia and industry for many years. Applications in DC have diverse requirements on communication. There are huge demands on data center network (DCN) control frameworks (CFs) for coordinating communication traffic. Simultaneously satisfying all demands is difficult and inefficient using existing traditional network devices and protocols. Recently, the agile software-defined Networking (SDN) is introduced to DCN for speeding up the development of the DCNCF. Application-awareness preserves the application semantics including the collective goals of communications. Previous works have illustrated that application-aware DCNCFs can much more efficiently allocate network resources by explicitly …