Digital Commons Network^™

Securing Connected And Autonomous Vehicles, Owana Marzia Moushi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. In this dissertation, we focused on detecting various insider attacks in vehicular networks to enhance the security of the network.

Our first contribution in this dissertation is the detection of both binary and multi-class data replay and data replay Sybil attacks in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated …

Real-Time Estimated Sequential Organ Failure Assessment (Sofa) Score With Intervals: Improved Risk Monitoring With Estimated Uncertainty In Health Condition For Patients In Intensive Care Units, Yan He, Qian Luo, Hai Wang, Zhichao Zheng, Haidong Luo, Oon Cheong Ooi

Research Collection Lee Kong Chian School Of Business

Purpose: Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk-scoring systems to aid monitoring, existing systems do not address uncertainties in risk assessments. We developed an enhanced risk monitoring framework based on commonly used systems like the Sequential Organ Failure Assessment (SOFA) score by incorporating uncertainties to improve the effectiveness of real-time risk monitoring in ICUs.Methods: This study included 5,351 patients admitted to the Cardiothoracic ICU in the National University Hospital in Singapore. We developed machine learning …

Zero Day Malware Detection With Alpha: Fast Dbi With Transformer Models For Real World Application, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

The effectiveness of an AI model in accurately classifying novel malware hinges on the quality of the features it is trained on, which in turn depends on the effectiveness of the analysis tool used. Peekaboo, a Dynamic Binary Instrumentation (DBI) tool, defeats malware evasion techniques to capture authentic behavior at the Assembly (ASM) instruction level. This behavior exhibits patterns consistent with Zipf's law, a distribution commonly seen in natural languages, making Transformer models particularly effective for binary classification tasks. We introduce Alpha, a framework for zero-day malware detection that leverages Transformer models, Support Vector Machines (SVMs) and ASM language features. …

Predicting Major Solar Flares Using Convolutional Neural Networks And Multivariate Magnetic Field Time-Series Data, Arash Azizian Foumani

All Graduate Theses and Dissertations, Fall 2023 to Present

Major solar flares are sudden, intense bursts of X-ray energy from the Sun, capable of severely impacting critical technological infrastructure like satellites, communication networks, and power grids on Earth. Accurate prediction of these high-intensity events is crucial but presents a significant challenge. This is largely due to their infrequent occurrence and the complex, dynamic nature of the Sun's underlying magnetic activity which drives these events. This research focuses on improving the prediction of major solar flares by utilizing detailed historical data that tracks the evolution of magnetic properties within solar active regions over time. This time-based data, however, contains inherent …

Privacy, Identity, And Fairness: Unpacking Ethical Influences On Metaverse Adoption In University Learning, Mousa Al-Kfairy, Meera Alalawi, Saed Alrabaee, Omar Alfandi

All Works

As immersive technologies like the Metaverse continue to reshape higher education, it becomes increasingly vital to examine the ethical dimensions shaping student engagement with these platforms. This study investigates how university students perceive privacy, digital identity, informed consent, and algorithmic fairness in Metaverse-based classrooms, and how these perceptions influence their trust and behavioral intention to adopt the technology. A quantitative survey was conducted with 310 university students, all of whom had prior exposure to virtual learning platforms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0, the study found that Metaverse Ethical Dimensions (MED) significantly influence both Trusting …

An Intelligent Healthcare System For Rare Disease Diagnosis Utilizing Electronic Health Records Based On A Knowledge-Guided Multimodal Transformer Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Ankur Pandey

All Works

Rare diseases are a common problem with millions of patients globally, but their diagnosis is difficult because of varied clinical presentations, small sample size, and disparate biomedical data sources. Current diagnostic tools are not able to combine multimodal information effectively, which results in a timely or wrong diagnosis. To fill this gap, this paper suggests a smart multimodal healthcare framework integrating electronic health records (EHRs), genomic sequences, and medical imaging to improve the detection of rare diseases. The framework uses Swin Transformer to extract hierarchical visual features in radiographic scans, Med-BERT and Transformer-XL to learn semantic and long-term temporal relations …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

All Works

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

All Works

Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Embedding-Driven Dual-Branch Approach For Accurate Breast Tumor Cellularity Classification, Hossam Magdy Balaha, Ali Mahmoud, Khadiga M. Ali, Mohammed Ghazal, Norah Saleh Alghamdi, Ashraf Khalil, Ayman El-Baz

All Works

This study proposes a dual-branch framework for precise classification of breast tumor cellularity via histopathological images where it integrates two distinct branches: the Embedding Extraction Branch (embedding-driven) and the Vision Classification Branch (vision-based). The Embedding Extraction Branch uses the Virchow2 transformation to generate dense, structured embeddings, whereas the Vision Classification Branch employs Nomic AI Embedded Vision v1.5 to process image patches and produce classification logits. Both branches’ outputs are combined to form the final classification. The framework also suggests Knowledge Block with fully connected layers, batch normalization, and dropout to improve feature extraction and reduce overfitting. The proposed approach reports …

Enhancing Smart Contract Security Using A Code Representation And Gan Based Methodology, Dileep Kumar Murala, Samia Loucif, K. Vara Prasada Rao, Habib Hamam

All Works

Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs …

Copyright Ownership And Duration Of Ai-Authored Works, Cheng Lim Saw

Research Collection Yong Pung How School Of Law

On the assumption that Parliament has endorsed the notion of AI authorship and the prospect that copyright may well subsist in works created autonomously by the AI itself, this essay further explores allied issues surrounding the ownership and duration of copyright in AI-authored works.

Big Data Transfer Service Architecture For Cloud Data Centers: Problems, Methods, Applications, And Future Trends, Muhammad Umar Majigi, Ismaila Idris, Shafi’I Muhammad Abdulhamid, Richard A. Ikuesan

All Works

Data volume, velocity, and structure have significantly evolved over the years. The complex networking architectures of current infrastructures, and the development, and accessibility of cloud services to a diverse user base have introduced numerous challenges which have raised concerns regarding the quality-of-service performance in data processing for both service providers and customers. Key issues identified in the context of big data transfer services for cloud data centers include storage, big data transfer, service transfer architecture, data processing, bandwidth, and security, all of which demand extensive research. After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data …

A Hybrid Fog-Edge Computing Architecture For Real-Time Health Monitoring In Iomt Systems With Optimized Latency And Threat Resilience, Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira

All Works

The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing …

A Deep Learning Framework For Automated Breast Cancer Diagnosis Using Intelligent Segmentation And Classification, Ahed Abugabah

All Works

Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution …

Connecting The Dots: Iot, Sustainability, And Sdgs, Saadat M. Alhashmi, Islam Al-Qudah, Ibrahim Abaker Hashem, Belal Alsinglawi, Raiza Borreo, Hassan S․ Migdadi, Weisi Chen

All Works

Internet of Things (IoT) technologies can transform various sectors by converging with global sustainability goals. This paper systematically reviews how IoT supports fulfilling the United Nations Sustainable Development Goals (SDGs). This study initially identified publications that are most relevant to IoT and sustainability. Each publication was carefully examined and mapped to its corresponding SDG, methodology, context, and country. This work presents an opportunity to learn about country contributions, collaborations, and IoT and SDG research trends over the past decade. India, China, and the US were among the top contributors to the IoT and SDG literature, with India accounting for 68 …

Security Vulnerabilities And Defense Tactics For Generative Ai Application Development, Kyle Klein

University Honors Theses

Generative AI (GenAI) applications such as OpenAI's ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program's responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. …

Qualitative Stereo Vision Using Distributed Extended Waltz Filtering, Ben Mathew

Theses and Dissertations

Stereo vision is a fundamental problem in computer vision, aimed at reconstructing three-dimensional scene structure from two or more two-dimensional images. Traditional stereo algorithms rely on quantitative disparity estimation, often constrained by calibration precision, lighting variations, and surface texture. In contrast, our proposed Qualitative Stereo Vision seeks to understand depth relationships and spatial configurations from multiple planar views through symbolic reasoning and constraint satisfaction, offering a more flexible and cognitively plausible approach to scene interpretation.

This dissertation presents a novel framework called Distributed Extended Waltz Filtering, designed to provide qualitative stereo vision, particularly in the presence of occlusions—a persistent challenge …

Discriminative And Generative Video Modeling, Anh Pha Nguyen

Graduate Theses and Dissertations

Video modeling stands at the core of modern computer vision, enabling progress in domains such as surveillance, autonomous driving, and instructional assistance. Yet the complexity of spatiotemporal dynamics, multimodal integration, and the need for scalable and generalizable models present significant challenges. This dissertation addresses these issues from three complementary perspectives: discriminative modeling, multimodal (vision + language) alignment, and generative approaches, contributing new methods, datasets, and paradigms for advancing video understanding. In the discriminative setting, we propose a domain-adaptive framework for crowd counting that employs entropy minimization and adversarial learning to improve cross-domain generalization, and introduce a single-stage global association method …

Programmable Network Approaches To Resilience And Security In Phasor Measurement Unit Networks, Zhiyao He

Graduate Theses and Dissertations

The security and resilience of smart grids are critical for ensuring reliable and stable power delivery. As modern power systems evolve to incorporate more advanced sensing and control capabilities, Phasor Measurement Units (PMUs) have become an important source of high-frequency, time-synchronized measurements that support wide-area monitoring, control, and protection. However, the growing complexity of smart grids and their reliance on real-time communication expose them to a range of cyber threats, including data loss, tampering, and coordinated attacks. This dissertation explores the use of programmable network technologies, particularly P4-based programmable switches, to provide in-network solutions that enhance the reliability and security …

Privacy Protection In Cloud-Based Biometric Systems, Yatish Reddy Dubasi

Graduate Theses and Dissertations

The widespread adoption of server-based biometric authentication systems, often hosted in the cloud, has introduced significant privacy risks. While these systems offer convenience, they require storing sensitive biometric templates on remote servers, creating a high-value target for adversaries. Unlike passwords, compromised biometric data is immutable and cannot be reissued, leading to an irreversible loss of privacy. This threat is exacerbated by template inversion attacks, which can reconstruct a user's original biometric trait (e.g., a face image) from its stored feature vector. This dissertation addresses these critical privacy challenges by designing, implementing, and evaluating a suite of novel frameworks for privacy-preserving …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Contextual Embedding Using Machine Learning For Cybersecurity: Access Control And Application, Thanh Bui

Graduate Theses and Dissertations

Access control is a well-established challenge in cybersecurity, with significant research focused on enhancing system autonomy and accuracy across various scenarios. Access control rules can be designed based on users’ roles, attributes, or relationships requesting access to specific resources. However, despite their benefits, these models still require human oversight. As systems expand and grow, it becomes increasingly complex for administrators to maintain precise access control rules, often necessitating extensive system updates or even a complete overhaul. This dissertation introduces a novel approach that leverages contextual embedding for user information to enable the system to autonomously authorize user requests for resources. …

Cnn Based Deep Learning Modeling With Explainability Analysis For Detecting Fraudulent Blockchain Transactions, Mohammad Hasan, Mohammad Shahriar Rahman, Mohammad Jabed Morshed Chowdhury, Iqbal H. Sarker

Research outputs 2022 to 2026

In the era of growing cryptocurrency adoption, Blockchain has emerged as a leading player in the digital payment landscape. However, this widespread popularity also brings forth various security challenges, including the need to safeguard against fraudulent activities. One of the paramount challenges in this regard is the detection of fraudulent transactions within the realm of Bitcoin data. This task significantly influences the trust and security of digital payments. Yet, it's a formidable challenge given the relatively low occurrence of fraudulent Bitcoin transactions. While deep learning techniques have demonstrated their prowess in fraud detection, there remains a scarcity of studies exploring …

Navigating Ai-Nature Frictions: Autonomous Vehicle Testing And Nature-Based Constraints, Prerona Das, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

In cities, the application of Artificial Intelligence (AI) is being directed towards transforming different aspects of urban life. These applications take material form in urban spaces, with autonomous vehicles (AVs) providing a prominent example. AI systems rely on large volumes of data on their surroundings to refine the algorithms and enhance the accuracy of prediction for operational efficiency and safety. However, such algorithmic learning and execution can present challenges when dealing with the unpredictable, complex, and dynamic aspects of urban spaces. Nature is a paradigmatic example of such unpredictability, because natural phenomena usually defy consistent patterns and precise data-based modelling. …

Fl-Cdf: Collaborative Defense Framework For Backdoor Mitigation In Federated Learning, Haiyan Zhang, Xinghua Li, Yinbin Miao, Shunjie Yuan, Mengyao Zhu, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module …

General Test-Time Backdoor Detection In Split Neural Network-Based Vertical Federated Learning, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in …

Kpiroot+: An Efficient Integrated Framework For Anomaly Detection And Root Cause Analysis In Large-Scale Cloud Systems, Wenwei Gu, Renyi Zhong, Guangba Yu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their runtime status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

Genscore: Agent-Based Short-Answer Question Generation And Scoring In Software Engineering Courses, Nguyen Binh Duong Ta, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Short-answer questions are commonly used in educational assessments, as they are often viewed as a more effective way than multiple-choice questions to determine whether students have achieved the intended learning outcomes. However, manually creating appropriate questions targeting different cognitive levels such as those defined by the Bloom’s Taxonomy, and grading text answers from students are not trivial tasks for instructors. Existing work on auto-question generation and scoring in computing education typically targets coding-based questions. However, in software engineering courses, assessments can extend beyond coding to understanding of processes, DevOps methodologies, system design, etc. This work aims to address the dual …

Reliable-Data-Split (Rds): Maximizing Model Potential With Reinforced Selection Strategy, Hoang D. Nguyen, Xuan-Son Vu, Quoc Tuan Truong, Duc-Trong Le

Research Collection School Of Computing and Information Systems

The nexus between data characteristics and parametric models is fundamental for developing effective and reliable artificial intelligence (AI) systems. Mismatches in data properties for model development may lead to deleterious effects on AI model performance in machine learning practice. This paper proposes a Reliable Data Split (RDS) procedure to learn how to select data points that will generalise the target domain adequately by employing prior knowledge of the data generative process. We introduce a reinforced selection strategy using deep reinforcement learning with diverse black box predictors in maximising ensemble rewards as the proxy of model performance potential while maintaining an …