Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation,
2025
Department of Computer Science and Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania
Design And Implementation Of Secured Hybrid Gateway Node For Securing Iot - Enabled Distribution Automation, Ally Bitebo
Tanzania Journal of Engineering and Technology (TJET)
The integration of smart grid and Internet of Things (IoT) technologies plays a crucial role in enhancing the quality of services provided by traditional electrical grids. This combination has enabled the introduction of new services, such as demand response, automatic meter reading, and IoT-enabled Distribution Automation (IoT-DA), which incorporates sensors, actuators, intelligent electrical devices (IEDs), and information and communication technologies to monitor and control the grid. However, this integration also introduces network security risks, including Denial of Service (DoS) attacks, false data injection, and masquerading attacks, such as system node impersonation that can transmit incorrect readings, trigger false alarms, and …
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms,
2025
Dakota State University
Scalable And Ethical Insider Threat Detection Through Data Synthesis And Analysis By Llms, Haywood Gelman, John Hastings
Research & Publications
Insider threats wield an outsized influence on organizations, disproportionate to their small numbers. This is due to the internal access insiders have to systems, information, and infrastructure. Signals for such risks may be found in anonymous submissions to public web-based job search site reviews. This research studies the potential for large language models (LLMs) to analyze and detect insider threat sentiment within job site reviews. Addressing ethical data collection concerns, this research utilizes synthetic data generation using LLMs alongside existing job review datasets. A comparative analysis of sentiment scores generated by LLMs is benchmarked against expert human scoring. Findings reveal …
Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code,
2025
Dakota State University
Comparing Unidirectional, Bidirectional, And Word2vec Models For Discovering Vulnerabilities In Compiled Lifted Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier
Research & Publications
Ransomware and other forms of malware cause significant financial and operational damage to organizations by exploiting long-standing and often difficult-to-detect software vulnerabilities. To detect vulnerabilities such as buffer overflows in compiled code, this research investigates the application of unidirectional transformer-based embeddings, specifically GPT-2. Using a dataset of LLVM functions, we trained a GPT-2 model to generate embeddings, which were subsequently used to build LSTM neural networks to differentiate between vulnerable and non-vulnerable code. Our study reveals that embeddings from the GPT-2 model significantly outperform those from bidirectional models of BERT and RoBERTa, achieving an accuracy of 92.5\% and an F1-score …
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 …
A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals,
2025
Dakota State University
A Survey-Based Quantitative Analysis Of Stress Factors And Their Impacts Among Cybersecurity Professionals, Sunil Arora, John D. Hastings
Research & Publications
This study investigates the prevalence and underlying causes of work-related stress and burnout among cybersecurity professionals using a quantitative survey approach guided by the Job Demands-Resources model. Analysis of responses from 50 cybersecurity practitioners reveals an alarming reality: 44% report experiencing severe work-related stress and burnout, while an additional 28% are uncertain about their condition. The demanding nature of cybersecurity roles, unrealistic expectations, and unsupportive organizational cultures emerge as primary factors fueling this crisis. Notably, 66% of respondents perceive cybersecurity jobs as more stressful than other IT positions, with 84% facing additional challenges due to the pandemic and recent high-profile …
Cybersecurity And Business Analytics: Strengthening Financial And Governmental Digital Defenses,
2025
Lewis University
Cybersecurity And Business Analytics: Strengthening Financial And Governmental Digital Defenses, Bushra F. Malik, Ravindar Reddy Gopireddy
Accounting, Business Analytics, Economics, and Finance Department Faculty Articles
The rapid digitization of financial services and federal operations has significantly increased cybersecurity risks. Cybercriminals are continuously evolving their attack methodologies, targeting financial institutions and government agencies to exploit vulnerabilities in digital infrastructures. Business analytics has emerged as a powerful tool in combating these threats by leveraging artificial intelligence (AI), machine learning (ML), and big data analytics to detect, analyze, and prevent cyber threats in real time. This paper explores the convergence of cybersecurity and business analytics, emphasizing their combined role in strengthening threat intelligence, fraud detection, incident response, and regulatory compliance. The research provides insights into emerging security trends, …
Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit,
2025
University of Texas at Arlington
Technology-Facilitated Abuse (Tfa): Analyzing Trends, Tactics, And Victim Responses On Reddit, Solomon G. Dandekar
Computer Science and Engineering Theses - Archive
The increasing integration of technology into daily life has provided numerous benefits but also significant risks, particularly when exploited by malicious actors in cases of technology facilitated abuse (TFA). Per- petrators can misuse technology to monitor, control, and intimidate their partners, random strangers, etc. exacerbating cycles of abuse. From location tracking and cellphone surveillance to smart device manipula- tion, spyware, and doxing, digital tools have become powerful instruments for coercion and control. This research project investigates the role of technology in stalking and harassment by analyzing discussions on a relevant subreddit where victims share their experiences, strategies for coping, and …
Scalable Approaches Towards Characterizing And Mitigating Emerging Phishing Scams,
2025
University of Texas at Arlington
Scalable Approaches Towards Characterizing And Mitigating Emerging Phishing Scams, Sayak Saha Roy
Computer Science and Engineering Dissertations - Archive
Phishing scams are among the most dangerous and persistent forms of cybercrime, leveraging social engineering to exploit human behavior and obtain sensitive information, leading to widespread identity theft and data breaches. In the past year, these attacks have resulted in financial losses exceeding $10 billion in the United States alone. As phishing scams continue to evolve, they have not only expanded in scale but also grown in sophistication, spreading rapidly across social media and employing adversarial techniques to evade detection by anti-scam tools. The situation is further exacerbated by the availability of advanced phishing kits, and more recently, generative AI, …
Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities,
2025
National University of Science and Technology "Politehnica" Bucharest
Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu
Engineering Management & Systems Engineering Faculty Publications
Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …
Scalable, Secure, And Adaptable Perception Systems Through Adversarial Analysis And Federated Fine-Tuning,
2025
University of Texas at Arlington
Scalable, Secure, And Adaptable Perception Systems Through Adversarial Analysis And Federated Fine-Tuning, Arkajyoti Mitra
Computer Science and Engineering Dissertations - Archive
Perception systems are fundamental to intelligent machines, enabling them to sense, understand, and interpret complex environments. However, as perception increasingly underpins critical applications such as autonomous vehicles, IoT healthcare devices, and smart trading platforms, challenges related to security, scalability, and environmental understanding have become more pressing. This work addresses three core research questions: (1) How can we identify, analyze, and mitigate adversarial vulnerabilities in perception systems to ensure reliable operation under adversarial conditions? (2.1) How can AVPS models be efficiently scaled and fine-tuned across decentralized and resource-constrained environments while preserving privacy and performance? (2.2) How can we scale generative models …
Understanding Pii Leakage In Large Language Models: A Systematic Survey,
2025
Zhejiang University
Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang
Computer Science Faculty Publications
Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved …
Privshap: A Finer-Granularity Network Linearization Method For Private Inference,
2025
Old Dominion University
Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu
Computer Science Faculty Publications
Private inference applies cryptographic techniques like homomorphic encryption, garble circuit and secret sharing to keep both sides privacy in a client-server setting during inference. It is often hindered by the high communication overheads, especially at non-linear activation layers such as ReLU. Hence ReLU pruning has been widely recognized as an efficient way to accelerate private inference. Existing approaches to ReLU pruning typically rely on coarse hypothesis, which assume an inverse correlation between the importance of ReLU and linear layers or shallow activation layers have less importance for universal models, to assign the budgets according to the layer while preserving the …
Tedvil: Leveraging Transformer-Based Embeddings For Vulnerability Detection In Lifted Code,
2025
Dakota State University
Tedvil: Leveraging Transformer-Based Embeddings For Vulnerability Detection In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu
Research & Publications
Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows …
Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs,
2025
Rice University
Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu
Psychology Faculty Publications
Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …
Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library,
2025
University of New Hampshire - Main Campus
Signal-Based Error Handling: Case Study Using The Bathymetric Attributed Grid Library, Anthony R. Papetti
Honors Theses and Capstones
No abstract provided.
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 …
A Spoofing Speech Detection Method Combining Multi-Scale Features And Cross-Layer Identification,
2025
Taiyuan University of Technology
A Spoofing Speech Detection Method Combining Multi-Scale Features And Cross-Layer Identification, Hongyan Yuan, Linjuan Zhang, Baoning Niu, Xianrong Zheng
Information Technology & Decision Sciences Faculty Publications
Pre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability …
Death By Design: A Biological Approach To Container Security,
2025
Eastern Washington University
Death By Design: A Biological Approach To Container Security, Alexander Hunter Moomaw
EWU Masters Thesis Collection
Microservice architectures are central to modern cloud computing and have become the dominant design pattern for scalable, distributed applications. Kubernetes is often the tool used for rapid deployment and orchestration of microservices. A known issue for large networks clusters is a malicious actor can compromise a vulnerable cluster in minutes. Their ability to quickly compromise vulnerable clusters highlights the urgent need for stronger security. This project views cluster defense through a preemptive security lens, introducing the Cluster Life cycle Management System (CLMS). Inspired by the biological process of apoptosis, CLMS systematically replaces aging containers and services to prevent exploitation of …
Data Injustice In Global Justice,
2025
Maurer School of Law - Indiana University
Data Injustice In Global Justice, Asaf Lubin, Cherry Tang
Articles by Maurer Faculty
In May 2020, the United Nations Secretary-General unveiled a sweeping “Data Strategy for Action by Everyone, Everywhere,” seeking to unlock the UN’s “full data potential.” The International Criminal Court’s Office of the Prosecutor followed suit, declaring in 2023 its intent to acquire advanced cyber forensic tools so as to hold the “widest range of digital evidence globally.” Across international institutions, data-driven governance has become the norm, with humanitarian agencies and tribunals transforming into “data hubs and information clearinghouses.” This Article critiques the unfettered datafication of global justice by international courts and organizations. These entities have aggressively expanded their data-driven operations …
A Systematic Review And Taxonomy For Privacy Breach Classification: Trends, Gaps, And Future Directions,
2025
Dakota State University
A Systematic Review And Taxonomy For Privacy Breach Classification: Trends, Gaps, And Future Directions, Clint Fuchs, John Hastings
Research & Publications
In response to the rising frequency and complexity of data breaches and evolving global privacy regulations, this study presents a comprehensive examination of academic literature on the classification of privacy breaches and violations between 2010-2024. Through a systematic literature review, a corpus of screened studies was assembled and analyzed to identify primary research themes, emerging trends, and gaps in the field. A novel taxonomy is introduced to guide efforts by categorizing research efforts into seven domains: breach classification, report classification, breach detection, threat detection, breach prediction, risk analysis, and threat classification. An analysis reveals that breach classification and detection dominate …
