Other Computer Engineering Commons^™

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik

Military Cyber Affairs

This article examines how hybrid deep learning can strengthen intrusion detection for military and defense networks. Using the CSE-CIC-IDS2018 dataset, the study evaluates a CNN-BiLSTM model designed to detect benign traffic and multiple attack categories, including DDoS, DoS, botnet, brute-force, web attack, and infiltration activity. The model achieved strong multi-class detection performance, with 0.9893 accuracy and 0.9979 ROC-AUC. The findings suggest that AI-supported intrusion detection can improve cyber defense operations, analyst triage, and protection of mission-critical networks.

Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin

Military Cyber Affairs

The increasing complexity of software and demands for rapid deployment have pushed the software industry to rely more on large language models (LLMs) in developing source code. However, as this technology is still relatively recent, questions can arise about the safety of the generated code. This paper presents an analysis of seven LLMs with respect to the presence of vulnerabilities within source code. Our findings show that LLMs are more likely to produce vulnerable web applications than vulnerable C programs, and that vulnerabilities are more likely to occur when program size and complexity increases.

Foreward, Todd Arnold

Military Cyber Affairs

No abstract provided.

Letter From The Director: Mastery In Practice, Joseph Schafer

Military Cyber Affairs

No abstract provided.

Ai Cyber First Responders: Bottleneck Analysis Of Heterogeneous Cpu–Gpu Pipelines For Security Operations Center Triage, Christine Pierce

Harrisburg University Other Works

Abstract — Modern Security Operations Centers (SOCs) must continuously process massive volumes of heterogeneous security telemetry while meeting stringent throughput, latency, and operational continuity requirements. Although transformer-based artificial intelligence has significantly improved threat detection and alert prioritization, most cybersecurity research evaluates model accuracy rather than the end-to-end behavior of AI-enabled operational pipelines. Consequently, relatively little is known about how heterogeneous CPU–GPU coordination, scheduling overhead, memory movement, and synchronization collectively influence operational SOC performance. This paper presents the AI Cyber First Responder, a heterogeneous SOC triage architecture that integrates GPU-accelerated transformer inference with CPU-based doctrine-driven reasoning to investigate end-to-end pipeline behavior …

Hyperchaotic Noise Generation For Adversarial Encryption In Privacy-Preserving Image Classification, Neeraja Beesetti

Master's Theses

Artificial intelligence systems make useful predictions by taking in data and returning a classification, recommendation, or decision. Obtaining that prediction, however, requires sharing the data first. This creates a fundamental privacy challenge in machine learning: users must expose their data to receive a valuable prediction. Machine learning systems increasingly rely on cloud-based image classification for this reason, transmitting images from edge devices to remote servers rather than running large models locally. This creates a conflict between the accuracy a classifier requires and the privacy a data owner wants. Traditional encryption destroys the image structure on which a classifier depends, while …

Developing A Natural Language Interface For Knowledge Graphs, Ruth Assefa, Sarah Mendoza, Luke Voinov, Oyku Serap Ogut, Nurcan Yuruk

SMU Journal of Undergraduate Research

This paper proposes to solve the challenge of making databases more user-friendly by interfacing them with OpenAI's ChatGPT-3.5 model. We implemented this solution to assist researchers in easily finding others with similar research interests. Our study involves 184 researchers from 14 departments at Southern Methodist University (SMU). We collected researchers' areas of expertise and biographies and stored them in a Neo4j graph database. We used OpenAI's embedding models to create vector representations of the collected data, allowing for accurate similarity assessments via Neo4j's built-in algorithms. By integrating this system with LangChain, we enabled natural language queries. The results demonstrated high …

Reproducing And Evaluating Charger Surfing: Robustness Of Smartphone Charging-Line Side-Channels, Colby M. Watts

Master's Theses

Smartphones are frequently connected to external, untrusted charging hardware, creating opportunities for side-channel attacks that do not require malware or direct access to device data. Charger Surfing, a recently proposed charging-line power analysis side-channel attack, reported high accuracy in inferring touchscreen input from voltage measurements collected from a smartphone’s charging cable; however, the reproducibility and robustness of these results under different conditions remain unclear. This thesis presents an independent replication and evaluation of Charger Surfing, including the development of an end-to-end data collection pipeline consisting of a modified charging cable, oscilloscope-based recordings, custom Android app, automated trace processing, and convolutional …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

Using Ali-4 Z-Implication In Controller Design, Shamil Azer Ahmadov

Chemical Technology, Control and Management

One of the widely used theories in the information processing is Professor Zadeh's fuzzy logic theory. Fuzzy implications form the basis of this theory. When processing information using fuzzy implication, the chosen judgment method and the type of implication affect the result. Referring to the review of the relevant literature on fuzzy implications, it can be noted that there are still unresolved problems and issues. For example, fuzzy implications cannot be used in processing imperfect information or information based on probabilistic and fuzzy uncertainty. Existing fuzzy implications face limitations in practical applications. Fuzzy implications only take into account inaccuracy, but …

Move Fast And Don’T Break Things: Collaborative Privacy Governance In Higher Education, Tyler Schroder, Chad Fenner

Research & Publications

Student-developed applications increasingly replicate or replace official university platforms, often prioritizing speed over security and privacy. This “shadow IT” ecosystem emerges from gaps in institutional tools and is amplified by AI-assisted development, which can introduce insecure defaults. These informal systems risk exposing FERPA‑protected or sensitive institutional data, as seen in student‑built directory and club‑information apps that redistributed restricted information more permissively than intended. While most universities lack clear governance mechanisms for student developers, Yale’s structured, student‑specific data‑use policy offers a notable model. This paper examines these risks and proposes a collaborative, API‑first framework that supports innovation while enforcing privacy, security, …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Comparative Evaluation Of Deep Learning Models: Resnet18, Minivgg, And Yolov8 For Five-Class Blood Cell Classification, Keita Sakurai

Master's Theses or Doctor of Nursing Practice

Accurate classification of blood cell types is a critical task in automated hematological analysis. This study presents a comparative evaluation of three deep learning architectures, ResNet18, MiniVGG, and YOLOv8, for five-class blood cell image classification. To ensure a fair comparison, all models were trained under standardized conditions, including a consistent 90:10 training–validation split, controlled dataset size, and fixed training epochs. ResNet18 was trained to establish a baseline using residual learning. MiniVGG employed a compact VGG-inspired design with regularization to balance efficiency and accuracy, while YOLOv8 leveraged a lightweight, pretrained classification backbone with integrated data augmentation. Experimental results demonstrate a clear …

Analyzing Network Traffic And Data Exfiltration Via Smb In Post-Vm Escape Scenarios, Noah M. Disanza

Williams Honors College, Honors Research Projects

Virtual machines (VMs) play a crucial role in modern IT infrastructure environments by providing isolation and enhanced security, among other things, for both personal and corporate systems. VMs are heavily rely upon to safely test malware, manage infrastructure, and reduce risk to host systems. This reliance is so substantial that the idea of reducing risk to the host system is believed to be erasing risk entirely. However, this mindset has shown to be challenged time and time again by the emergence of exploits known as virtual machine escapes. These exploits allow malicious actors to break out of the virtualized environment …

Comprehensive Performance Evaluation Of Devops Infrastructure Under Dynamic Workloads, Abdulrazaq Mamud

College of Graduate Studies: Theses & Dissertations

This research aims to investigate performance optimization and reliability issues related to cloud-based computing environments through an analysis of three key infrastructure components: virtualized CPU resource management, distributed API rate limiting, and web server deployment architectures. This research combines machine learning and system experimentation as a way of exploring the impact of infrastructure-level behaviors on overall system performance and scalability. The first component of the research focuses on analyzing CPU Fragmentation in Virtualized Environments, where unbalanced workload allocation on Virtual CPU Cores causes increased tail latency, resulting in Service Level Agreement violations. Metrics are analyzed using the Random Forest classifier …

Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research …

Implementing Controls To Mitigate The 2013 Target Data Breach: A Cost Benefit Analysis, Pricilia R. Fajong

Dissertations, Theses, and Projects

In 2013 Target had a data breach, which compromised 40 million credit/debit card accounts and 70 million customer records. The attackers exploited a vulnerability in a third-party vendor (Fazio Mechanical Services), to gain access to Target's systems. The breach cost Target over $250 Million (USD) in legal fees, investigation expenses, and reputational damage (Jones, 2025). Based on inflation rate, the 2013 Target data breach would cost over $340 Million (USD) today. In this study, a cost-benefit analysis was done to determine whether it would have been more cost-effective for Target to have invested in security controls rather than paying for …

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 …

Toward A Generalizable Perceptual Hashing Framework For Image Manipulation Detection, Priyanka Samanta

Dissertations, Theses, and Capstone Projects

This thesis contributes to research in adversarial image manipulation detection. The primary motivation is the increasing need to verify digital images, especially for legal evidence, journalistic proof, or social media content—where manipulated or fabricated images can mislead, defame, or distort reality. A key application and contribution of this work is the development of eWitness, a blockchain application that generates and registers image provenance at capture time to enable independent verification of authenticity. The secret sauce behind the system is SmartHash, a novel and efficient perceptual hashing algorithm designed for real-world deployment in systems like eWitness. Unlike existing algorithms, SmartHash targets …

Multimodal Learning In Real-World Application: Enhancing Feature Representation And Training Strategies, Nana Lin

Graduate Doctoral Dissertations

Multimodal learning has emerged as a critical paradigm for developing intelligent systems that can understand and reason across diverse inputs such as images, text, and audio data. Despite significant advances, effective deployment of multimodal models in practice remains a challenging task. This dissertation explores how multimodal learning can be effectively applied to high-stakes, real-world scenarios, with a focus on enhancing feature representation and training efficiency. Specifically, this research investigates multimodal learning strategies in two key domains: healthcare and surveillance.

In the healthcare domain, we explored the data fusion and alignment approaches for cognitive decline diagnoses. First, we propose the LOVEMA …

Smart Mobility Technologies In Urban Areas Of Emerging Economies: A Bibliometric Analysis, Peter Mugisha, Rose Luke, Joash Mageto, Hossana Twinomurinzi

African Conference on Information Systems and Technology

Despite the adoption of smart mobility solutions in emerging economies, challenges such as traffic congestion, pollution and inadequate infrastructure still persist. This study analyses 540 scholarly articles published between 2003 and 2024 to evaluate how smart mobility technologies – such as Intelligent Transportation Systems (ITS), Internet of Things (IoT) and Artificial Intelligence (AI) – have been implemented in these regions. Data was retrieved from Scopus and Web of Science and analysed using Biblioshiny for bibliometric mapping and Atlas.ti for thematic analysis. The review identifies research trends and gaps, showing how ITS has improved transport management in cities like Nairobi, and …

Fourier-Feature Mlp Toolkit For Gpu-Accelerated Cardiac-Mri 4dcmr Strain Analysis, Aarnav T. Sabale, Marco A. Prado, Craig J. Goergen

Discovery Undergraduate Interdisciplinary Research Internship

This paper explores the embedding of a Fourier-Feature—enhanced multiplayer perceptron(MLP-FEE) at the heart of a newly refactored python workflow for four-dimensional cardiac-MRI strain quantification demonstrating how a single, compact network can outperform traditional convolution and spline-based methods. The original code, capable of orientation normalization, displacement tracking, and finite-difference strain computation, has been translated and consolidated into pytorch. By injecting sinusoidal positional encodings at the network’s input layer supplied a rich set of high-frequency basis functions hence enabling multilayer MLP to resolve gradients that cubic splines and conventional CNNs typically blur or struggle with. Profiling on an Apple-silicon GPU shows interactive …

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, …

Director, Military Cyber Institute, Joseph Schafer

Military Cyber Affairs

No abstract provided.

Throughput Of Ascon Compared With Popular Iot Encryption Algorithms, Mitchel R. Harvey (Ryan), Andrew M. Kaiser, Garrett W. Hoiness

Military Cyber Affairs

No abstract provided.

Anomaly Detection Of Network Layer Attacks Against Cyber Physical Systems Using Machine Learning And Deep Learning Techniques, James Alger, Michael Tu

Military Cyber Affairs

This research paper presents the analysis of using machine learning and deep learning algorithms on detecting anomalous network traffic in Cyber-Physical Systems (CPS). Using a real PLC CPS-based system, normal and anomalous network traffic will be captured using Wireshark. The research analyzes a DDoS attack. The focus of the research is to identify the most effective feature combinations and evaluate them on ML and DL models. The emphasis is on enhancing detection strategies rather than exploiting device vulnerabilities. The detection of network attacks often involves handling a vast array of high-level features. Previous studies (Li & Chasaki, 2022) apply machine …