Computer Engineering Commons^™

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 …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

Enhanced Intrusion Detection Using Recurrent Neural Networks With Amino Acid Codon Features, Omar Fitian Rashid, Mohammed Ahmed Subhi, Safa Ahmed Abdulsahib, Mohammed Khaleel Hussein, Marwan Ali Albahar

Iraqi Journal for Computer Science and Mathematics

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which …

Trends In Non-Profit Cybersecurity: Analyzing Three Years Of Incident Data From The Npcir, Stanley Mierzwa, Joanna Paliszkiewicz, Edyta Skarzyńska

Center for Cybersecurity

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly …

Trust And Pre-Employment Background Checks When Onboarding And Maintaining Information Security And Cybersecurity Staff, Stanley Mierzwa

Center for Cybersecurity

The realm of trust is broad and can include many facets that are difficult to capture and catalog. In relation to the work roles of information security and cybersecurity, the intersection of trust in human resource management is critical and an evolving area within most modern organizations, in almost any sector, and of any size. A foundational element of trust is fundamental to effective mission and work roles in information security and cybersecurity, as well as to every employee tasked with contributing to the security of an organization’s assets. This chapter will include sections on the role trust can and …

An Ai-Integrated Methodology For Secure Software And System Development, Ian Matthew Campbell Coston

Electronic Theses and Dissertations

Securing interconnected software systems requires more than layering existing frameworks on top of each other. Most current Secure Software and System Development Lifecycle (S-SDLC) models treat security as a phase rather than a design condition, leaving real gaps in governance, access control, and automated enforcement that become critical failure points in Internet of Things (IoT) environments where devices are resource-constrained, long-lived, and frequently insecure by default.

This dissertation introduces the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, a novel approach that unifies DevSecOps automation, the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF), and …

Research Days: Case Study: Creation Of A Student-Driven Radio Talk Show To Answer Cybersecurity Questions And Concerns To Boost Power Skills, Joel Leiva, Anthony Bayate, David Abiandu, Keith Fernandez

Center for Cybersecurity

Power skills are essential in any professional career. Oftentimes, college students don’t feel prepared enough to enter the workforce. Having good power skills in a group can greatly increase production and efficiency. This case study aims to develop these power skills in a group of college students through the creation of a student-driven radio talk show answering cybersecurity questions and concerns. A qualitative approach was used via the creation of the C.Y.B.E.R. radio show. This show enhanced the participants’ power skills such as collaboration, teamwork, and communication skills. The findings from this case study prove the alternate hypothesis of boosting …

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 …

Modern (Networked) Warfare: Its Concepts, Evolution, And Issues, Ramesh Subramanian

Journal of International Technology and Information Management

The on-going Russo-Ukraine war (February 2012-present), the recent India-Pakistan war (May 7-10, 2025), and the Israel-US attacks on Iran (June 13, 2025) have become interesting for a variety of reasons, and garnered a lot of attention in international defense publications and weapons manufacturers. To many defense analysts and defense industry professionals, these wars have become real-life testing platforms to examine and evaluate various present-day war technologies. They have opened up discussion on the future of war, the increasing use of inter-connected networks in warfare, and the increasing use of remote-operated drones in conducting surveillance and in offensive actions against …

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 …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Construction Of A Unified Knowledge Graph For Cyber Threat Intelligence, Moaz Usama Hassan Mr, Khaled , Nagaty, Noura Elmaghawry

Computer Networks

The rapid expansion and variety of cyber-threat information put enormous pressure on security operations centers (SOCs) that must convert unstructured data into understandable signals and make decisions upon it. This paper develops a Cyber-Threat-Intelligence (CTI) framework that integrates vulnerability information, product inventories, and weakness taxonomies into a domain-specific knowledge graph via automatic fusing. The proposed solution covers 284,296 CVEs, 101,644 CPE identifiers, and 965 CWE weaknesses, generating more than 800,000 typed edges linking threats, assets, tactics, and mitigations in an integrated CTI Knowledge graph. The graph was cross validated against four external standard datasets achieves full coverage of ATT&CK CAPEC, …

Deep Learning-Based Fog-Cloud Approach Intrusion Detection System In Iomt, Yahya Rbah, Mohammed Mahfoudi, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime Elbekkali

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) creates an interconnected environment linking humans, devices, sensors, and systems, enhancing healthcare services through advanced technologies. Nonetheless, these IoMT devices are susceptible to cyberattacks, which can endanger patient safety and healthcare services. To identify and mitigate cyberattacks in IoMT, techniques such as threat intelligence, log monitoring, and intrusion detection systems are employed. As attackers evolve their strategies, there is a growing trend towards leveraging artificial intelligence to achieve more predictive and accurate attack detection. Since IoMT devices are inherently low-power, they require minimal computing resources. Existing intrusion detection systems are generally trained in the …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi

Iraqi Journal for Computer Science and Mathematics

The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness …

The Future Of Al-Driven Cybersecurity For Advanced Iot, Estqlal Hammad Dhahi, Sanaa Hammad Dhahi, Ohood Fadil Alwan

Al-Esraa University College Journal for Engineering Sciences

Internet of Things technologies experience rapid advancement because of 5G networks and upcoming 6G technologies, which resulted in transformational changes to security dynamics. This study examines the functionality of artificial intelligence through platforms developed to secure Internet of Things systems. The demand for improved security capabilities has become essential because IoT devices generate new assault channels, and their market penetration speed is escalating. Machine learning algorithms, together with deep learning and natural language processing methods, are investigated in this paper for enhancing the security protocols of IoT systems through studies found in academic literature. The paper explores upcoming developments and …

Advanced Strategies And Solutions Towards More Secure And Effective Two-Factor Authentication In Networking, Zahraa Sameer Jawad

Al-Esraa University College Journal for Engineering Sciences

With the rapid increase in cybersecurity threats targeting network systems, traditional two-factor authentication (2FA) methods are insufficient to address advanced attacks. Vulnerabilities such as phishing, SIM-swapping, and social engineering exploit the limitations of SMS-based and email-based 2FA. This paper examines advanced strategies and solutions for securing networked environments through robust 2FA mechanisms, focusing on approaches like elliptic curve cryptography (ECC), digital certificates, and biometric verification. This article offers a comparative review of various strategies about their effectiveness in enhancing security, while also highlighting their capacity to optimize user-friendliness and adaptability to emerging threats. Research findings promote an effective countermeasure strategy …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb

Iraqi Journal for Computer Science and Mathematics

In an increasingly interconnected world, cybersecurity threats have become more sophisticated, necessitating advanced, scalable, and privacy-preserving solutions. MetaGuard emerges as a novel framework that integrates federated learning with hybrid machine learning models, specifically XGBoost and meta-learning, to enhance proactive cyber threat detection. This framework offers a robust, distributed approach to cybersecurity, ensuring high detection accuracy while preserving user privacy through the implementation of differential privacy and homomorphic encryption. MetaGuard leverages distributed nodes to collaboratively train a global model, enabling rapid adaptation to new threats without the need for centralized data aggregation. Experimental evaluations using the CYBER-2024 dataset demonstrate that MetaGuard …

Internet Of Things And Modern Digital Evidence Collection, Muhammad T. Haider

Student Theses

The ever-evolving landscape of technology and its innovations are populating our houses, streets and all kinds of industries. The use of smart devices is booming from most developed nations to underdeveloped countries. The complications which come with the use of the Internet of Things has been an active discussion for the past many years. If we look around in a room of 30 people, we will most likely find double the amount of IoT devices than the people in that room. All of those devices are connected to the Internet, and are communicating with data servers across the world. The …

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 …

Using Blockchain Technology To Help Secure America's Defense Critical Infrastructure, Vimal Buck, Aerin Krebs, Brynn Hillard, Jakob Gerha, Joseph Lutma, Srikar Maduposu, Ted Allen

Military Cyber Affairs

Critical water infrastructure in the United States faces increasing cybersecurity threats from state-sponsored actors, with potentially devastating consequences for national security, economic stability, and public health. (Cybersecurity and Infrastructure Security Agency, 2025). This infrastructure supports defense critical assets and is actively being targeted by various state-sponsored hacking groups, which poses a major concern for civilians and military alike. K. Herath (personal communication, February 24, 2025) reported being aware of two attacks on Ohio water systems during his tenure as Cybersecurity Strategic Advisor to Ohio Governor Mike DeWine.

Water is essential to everyday life and defense and presents as a high-value …

Harnessing Generative Ai And Large Language Models For Revolutionizing Cybersecurity In The Internet Of Things: Ethical And Privacy Implications, Harsha Sammangi, Aditya Jagatha, Jun Liu

Research & Publications

Generative artificial intelligence (AI) and large language models (LLMs) have in- troduced transformative capabilities in cybersecurity, particularly in securing Internet of Things (IoT) environments. These technologies can synthesize vast datasets, support real-time anomaly detection, and generate predictive insights through simple prompts. However, their deployment also presents ethical and privacy-related concerns, including algorithmic bias, data leakage, and misuse for malicious content creation. This paper conducts a systematic literature review to evaluate how LLMs and generative AI contribute to IoT cybersecurity. We propose an ethical AI-IoT security framework, examine key challenges, and offer recommendations for integrating responsible AI governance. We aim to …

Enhancing Cybersecurity Based On Blockchain Technology: A Systematic Review, Sarah Mohammed Shareef, Rehab Flaih Hassan

Journal of Soft Computing and Computer Applications

Cybersecurity is a crucial component of the security system that guards against unauthorized access to digital transactions. Blockchain is a decentralized ledger used to securely exchange digital currencies and conduct trades and transactions. Blockchain technology has led to significant changes in electronic transactions. The enormous potential is being exploited in many areas such as financial services, real estate, supply chain, and the Internet of Things. Despite being a security system, it has suffered from security threats to sensitive data. Phishing and 51% attacks can circumvent blockchain security, highlighting the need for thorough user education and awareness. Additionally, blockchains based on …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari

School of Computing: Dissertations, Theses, and Student Research

The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …

Privacy Implications Of Data Collection In Android Automotive Os, Bulut Gözübüyük

All Theses

Modern vehicles have become sophisticated computational and sensor systems, as evidenced by advanced driver assistance systems (ADAS), in-car infotainment, and autonomous driving capabilities. They collect and process vast amounts of data through various onboard subsystems. One significant player in this landscape is Android Automotive OS (AAOS), which has been integrated into over 100 million vehicles and has become a dominant force in the in-vehicle infotainment (IVI) market. With this extensive data collection, privacy concerns have become increasingly crucial. The volume of data gathered by these systems raises questions about how this information is stored, used, and protected, making privacy a …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …