Turbulence Prediction Using Non-Linear Phase Space Analysis,
2025
University of South Alabama
Turbulence Prediction Using Non-Linear Phase Space Analysis, Jeremy Quijano
Shelby Hall Graduate Research Forum Posters
Our research presents a novel approach for turbulence prediction in computational fluid dynamics (CFD) simulations using a non-linear phase space analysis (NLPSA) and threshold algorithm. NLPSA has been utilized in medical applications to predict seizures, as well as in cybersecurity to detect malicious control and utilization of computing systems. NLPSA uses time-series data to learn the normal operating state of the system, then sets a threshold to predict when the system becomes abnormal. Turbulence prediction is similar, such that a fluid system changes from normal to abnormal. Turbulence prediction methods currently utilize machine learning tools, such as convolutional neural networks …
Out-Of-Band Anomaly Detection For Real Time Operating Systems,
2025
University of South Alabama
Out-Of-Band Anomaly Detection For Real Time Operating Systems, Jeff K. Holifield
Shelby Hall Graduate Research Forum Posters
Real Time Operating Systems (RTOS) are increasing present throughout the industrial, business, defense, and healthcare spaces. These lightweight and efficient operating systems are designed to run on embedded, resource constrained devices, often within cyber-physical systems (CFS). A defining characteristic of RTOSs is that they are deterministic. Tasks are scheduled to run on fixed timelines within guaranteed execution windows. To accomplish tasks on time, real time software must conform to worst case execution times (WCETs) as design parameters. WCET is the maximum time a particular task can take to complete. Exceeding the WCET could cause system failure and lead to damage, …
Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems,
2025
University of South Alabama
Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya
Shelby Hall Graduate Research Forum Presentations
Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.
Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection,
2025
Nanjing University
Deepvmunprotect: Neural Network-Based Recovery Of Vm‑Protected Android Apps For Semantics‑Aware Malware Detection, Xin Zhao, Mu Zhang, Xiaopeng Ke, Yu Pan, Yue Duan, Sheng Zhong, Fengyuan Xu
Research Collection School Of Computing and Information Systems
The emerging virtual machine-based Android packers render existing unpacking techniques ineffective. The state-of-the-art unpacker falls short because it relies on unreliable heuristics and manually crafted semantic models. Hence, it cannot precisely recover app semantics necessary for malware detection. In this paper, we propose DeepVMUnProtect, a deep learning-based approach to automatically and accurately capture the semantics of VM-packed code, so as to facilitate semantic-based Android malware classification. Experiments have shown that DeepVMUnProtect outperforms the state-of-the-art tool on recovering opcode semantics in Qihoo(58.3%), Baidu(47.5%) and NMMP (58.8%) respectively, and can enable semantics-aware malware detection which prior work fails to do.
Using Machine Learning Models To Improve The Cyber Physical Security Of Drones,
2025
University of South Alabama
Using Machine Learning Models To Improve The Cyber Physical Security Of Drones, Sean Lee, Aviv Segev
Shelby Hall Graduate Research Forum Posters
This research proposes a new manner of implementing machine learning models such that, when applied on a drone, it will be able to accurately identify and maintain the authenticity of the entity sending the control data to the drone. To begin with, the drone will, for a pre-determined amount of signals received per unit time, determine the average signal strength (RSSI) of them and use that average to determine the approximate distance between the drone and the source of those signals. This single data point will be fed into a custom implementation of the SCluStream algorithm (a real-time clustering machine …
An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation,
2025
Singapore Management University
An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation, Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang
Research Collection School Of Computing and Information Systems
Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, …
Explainable Neural Networks With Guarantees: A Sparse Estimation Approach,
2025
Singapore Management University
Explainable Neural Networks With Guarantees: A Sparse Estimation Approach, Antoine Ledent, Peng Liu
Research Collection School Of Computing and Information Systems
Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …
Drone Delivery Network Design With Uncertainties,
2025
Singapore Management University
Drone Delivery Network Design With Uncertainties, Wenjia Zeng, Jiang Ruiwei, Hai Yang, Hai Wang
Research Collection School Of Computing and Information Systems
Unmanned aerial vehicles (UAVs), also called drones, are gaining popularity as an alternative delivery mode due to their faster delivery speed and reduced labor costs. Several companies, especially e-commerce giants, are conducting pilot projects that use drones to deliver fast food and groceries. In 2021, for example, Walmart partnered with Zipline in the United States to provide delivery services for areas near Walmart stores in Arkansas. In China, Meituan drone delivery services have been launched in Shenzhen and have conducted trial food delivery that cover more than 8,000 households.
Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan,
2025
Virginia Commonwealth University
Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan, Amy Corning, Jonathan D. Becker, Jon Graham, James Carrigan, Keisha Tennessee
ICRE Publications
Over the past two decades, Virginia has invested substantially in STEM education, in part through specialized programs focused on computer science and information technology (CS/IT). This study represents the first effort to identify Virginia’s specialized secondary CS/IT programs and examine them collectively. Findings from the statewide environmental scan indicate that the programs are delivered through a wide variety of institutional structures, including Governor’s STEM Academies, Governor’s Schools, specialty centers, and academies, but most often through Career and Technical Education (CTE) centers. Programs tend to be concentrated in metropolitan areas, and some rural divisions may not be served. The programs provide …
Rotary Operations Management & Automation Platform (Romap): Modernizing Attendance Tracking And Data Submission For Rotary Clubs,
2025
University of Texas at Arlington
Rotary Operations Management & Automation Platform (Romap): Modernizing Attendance Tracking And Data Submission For Rotary Clubs, Zaineel Mithani
2025 Fall Honors Capstones Projects - Archive
As Technical Lead of the Rotary Operations Management & Automation Platform (ROMAP), my Honors contribution focused on developing a Bluetooth Low Energy proximity-based attendance system enabling automatic, hands-free member check-ins. I researched and selected beacon hardware, designed RSSI-based distance calculation algorithms, and implemented platform-specific background processing for iOS and Android, achieving 97% detection accuracy. Beyond this Honors component, I architected the complete backend infrastructure including a Node.js API with 20+ endpoints, PostgreSQL database with Prisma ORM, and JWT authentication. I also developed a novel GPT-4 Vision automation system that intelligently populates web forms through computer vision, achieving 95% success rate …
5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network,
2025
Taiyuan University of Technology
5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network, Xin Liu, Xing Fan, Baoning Niu, Xianrong Zheng
Information Technology & Decision Sciences Faculty Publications
The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant …
Silent Sabotage: Identifying And Preventing Cyber Attacks From Inside Actors,
2025
The University of Akron
Silent Sabotage: Identifying And Preventing Cyber Attacks From Inside Actors, Autumn Groen
Williams Honors College, Honors Research Projects
Cyber-attacks are becoming increasingly common and damaging as technology advances each year. Many businesses cannot afford the latest security technologies, and even with the highest security measures, there can still be room for employee error or insider threats that are not taken into account. It is crucial to keep these factors in mind when securing a business network of any size or financial standing.This project will aim to simulate a business environment by first building a small network with three routers and a switch, and implementing some of the common best practices for network hardening from credible organizations like NIST …
Deep Reinforcement Learning With Explicit Context Representation,
2025
Cardiff University
Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Research Collection School Of Computing and Information Systems
Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …
Dynamic Analysis Of Malware Detection Using Customized Payloads: Examining The Effectiveness Of Manual And Automated Approaches In Web Applications,
2025
Georgia Southern University
Dynamic Analysis Of Malware Detection Using Customized Payloads: Examining The Effectiveness Of Manual And Automated Approaches In Web Applications, Jiban Krisna Das
College of Graduate Studies: Theses & Dissertations
Web applications are becoming the prime targets for cyber-attacks, where SQL injection (SQLi) and Cross Site Scripting (XSS) are the most exploited vulnerabilities. The study explores a novel approach using customized payloads to examine the effectiveness of manual and automated techniques of malware detection. This dynamic approach can effectively generate attack payloads and identify the vulnerabilities in a website thereby strengthening website security measures. This research focuses on dynamic analysis in a controlled environment while testing and analyzing SQL and XSS payloads under varying security conditions. This quantitative analysis involves crafting targeted payloads to bypass Web Application Firewall (WAF) filters …
A Comprehensive Academic And Industrial Survey Of Blockchain Technology For The Energy Sector Using Fuzzy Einstein Decision-Making,
2025
University of York
A Comprehensive Academic And Industrial Survey Of Blockchain Technology For The Energy Sector Using Fuzzy Einstein Decision-Making, Umit Cali, Annabelle Lee, Barry Hayes, Claudio Lima, D. Jonathan Sebastian-Cardenas, David Flynn, Emre Kantar, Farrokh Rahimi, Kaung Si Thu, Marco Pasetti, Marthe Fogstad Dynge, Merlinda Andoni, Muhammet Deveci, Murat Kuzlu, Raquel Alanso, Kim-Kwang Raymond Choo, Sambeet Mishra, Shammya Shananda Saha, Sonam Norbu, Srinikhil Gourisetti, Ugur Halden, Vahid Hosseinezhad, Valentin Robu
Engineering Technology Faculty Publications
The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in …
5g-Enabled Iot Gateway For Educational Purposes,
2025
Old Dominion University
5g-Enabled Iot Gateway For Educational Purposes, Murat Kuzlu
Engineering Technology Faculty Publications
The project aims to develop and implement a 5G-IoT gateway for efficient management and interconnection of IoT devices through advanced sensing and communication technologies. Sensing technologies encompass modern approaches for detecting and measuring physical properties with high accuracy across manufacturing, smart grids, healthcare, smart cities, and other domains. Communication technologies represent the latest developments in high-speed data transmission, offering enhanced reliability and network capacity. This 5G-IoT gateway functions as an educational platform, providing students and educators with hands-on experience in emerging technologies. The system creates opportunities for practical learning and research in telecommunication technologies by enabling direct engagement with 5G …
A Comprehensive Exploration Of 6g Wireless Communication Technologies,
2025
University of North Carolina at Charlotte
A Comprehensive Exploration Of 6g Wireless Communication Technologies, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md. Shahriar Uzzal, H. M. Dipu Kabir
Electrical & Computer Engineering Faculty Publications
As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into …
Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach,
2025
University of Isfahan
Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty
Center for Secure and Intelligent Critical Systems (CSICS) Publications
Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …
A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks,
2025
University of West Georgia
A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks, Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty
Center for Secure and Intelligent Critical Systems (CSICS) Publications
Sixth-generation (6G) wireless networks will become vulnerable due to native generative AI (GenAI)-driven intelligent poisoning attacks in both the radio unit and the core network. In particular, network parameters and metrics in cross-layer design pose fundamentally uncertain conditions and can be compromised through the native GenAI mechanism, which leverages data augmentation and reconstruction capabilities. This work investigates the capabilities of native GenAI to create novel poisoning attacks in wireless networks, while investigating their impact through uncertainty-informed root analysis. Then, detected attacks are mitigated by developing a trustworthy service aggregation in the wireless network. First, a joint decision problem is formulated …
Github Repository Complexity Leads To Diminished Web Archive Availability,
2025
Old Dominion University
Github Repository Complexity Leads To Diminished Web Archive Availability, David Calano, Michael Nelson, Michele Weigle
Computer Science Faculty Publications
Software is often developed using versioned controlled software, such as Git, and hosted on centralized Web hosts, such as GitHub and GitLab. These Web hosted software repositories are made available to users in the form of traditional HTML Web pages for each source file and directory, as well as a presentational home page and various descriptive pages. We examined more than 12,000 Web hosted Git repository project home pages, primarily from GitHub, to measure how well their presentational components are preserved in the Internet Archive, as well as the source trees of the collected GitHub repositories to assess the extent …
