arXiv subjects

Murat Ozer

Publications and source records attributed to Murat Ozer.

At least 19 recordsLinked to original sources

arXiv2026

Geographic Patterns in I2P Peer Selection: An Empirical Network Topology Analysis

The Invisible Internet Project (I2P) routes data via encrypted, decentralized tunnels. Peer selection can significantly affect security and performance. This empirical study examines whether geographic location systematically influences I2P's routing topology. Consistent with I2P's design principles, which include avoiding multiple peers from the same /16 IP subnet to maximize anonymity, we conducted assortativity analysis, community detection, and permutation testing on data from 327 routers and 254 connections (SWARM-I2P). We found a network-level absence of significant geographic homophily. The assortativity coefficient was r = 0.017 (p = 0.222). Same-country connections (11.1%) are statistically near random expectation (10.91%). Community detection found 110 highly modular groups (Q = 0.972) only moderately aligned geographically (NMI = 0.521). We conclude that aggregate peer selection in I2P leads to a highly heterogeneous, random geographical mixing, providing a foundation for understanding the performance-anonymity tradeoff.

cs.NI↗

arXiv2026

Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training

As Artificial Intelligence (AI) and Agentic AI become increasingly integrated across sectors such as education and healthcare, it is critical to ensure that Multi-Agent Education System (MAES) is explainable from the early stages of requirements engineering (RE) within the AI software development lifecycle. Explainability is essential to build trust, promote transparency, and enable effective human-AI collaboration. Although personas are well-established in human-computer interaction to represent users and capture their needs and behaviors, their role in RE for explainable MAES remains underexplored. This paper proposes a human-first, persona-driven, explainable MAES RE framework and demonstrates the framework through a MAES for clinical reasoning training. The framework integrates personas and user stories throughout the RE process to capture the needs, goals, and interactions of various stakeholders, including medical educators, medical students, AI patient agent, and clinical agents (physical exam agent, diagnostic agent, clinical intervention agent, supervisor agent, evaluation agent). The goals, underlying models, and knowledge base shape agent interactions and inform explainability requirements that guided the clinical reasoning training of medical students. A post-usage survey found that more than 78\% of medical students reported that MAES improved their clinical reasoning skills. These findings demonstrate that RE based on persona effectively connects technical requirements with non-technical medical students from a human-centered approach, ensuring that explainable MAES are trustworthy, interpretable, and aligned with authentic clinical scenarios from the early stages of the AI system engineering. The partial MAES for the clinical scenario simulator is~\href{https://github.com/2sigmaEdTech/MAS/}{open sourced here}.

cs.SE↗

arXiv2026

Detecting Fileless Cryptojacking in PowerShell Using AST-Enhanced CodeBERT Models

With the emergence of remote code execution (RCE) vulnerabilities in ubiquitous libraries and advanced social engineering techniques, threat actors have started conducting widespread fileless cryptojacking attacks. These attacks have become effective with stealthy techniques based on PowerShell-based exploitation in Windows OS environments. Even if attacks are detected and malicious scripts removed, processes may remain operational on victim endpoints, creating a significant challenge for detection mechanisms. In this paper, we conducted an experimental study with a collected dataset on detecting PowerShell-based fileless cryptojacking scripts. The results showed that Abstract Syntax Tree (AST)-based fine-tuned CodeBERT achieved a high recall rate, proving the importance of the use of AST integration and fine-tuned pre-trained models for programming language.

cs.CR↗

arXiv2025

Identifying Information Technology Research Trends through Text Mining of NSF Awards

Information Technology (IT) is recognized as an independent and unique research field. However, there has been ambiguity and difficulty in identifying and differentiating IT research from other close variations. Given this context, this paper aimed to explore the roots of the Information Technology (IT) research domain by conducting a large-scale text mining analysis of 50,780 abstracts from awarded NSF CISE grants from 1985 to 2024. We categorized the awards based on their program content, labeling human-centric programs as IT research programs and infrastructure-centric programs as other research programs based on the IT definitions in the literature. This novel approach helped us identify the core concepts of IT research and compare the similarities and differences between IT research and other research areas. The results showed that IT differentiates itself from other close variations by focusing more on the needs of users, organizations, and societies.

cs.DL↗

arXiv2025

Extreme Learning Machine Based System for DDoS Attacks Detections on IoMT Devices

The Internet of Medical Things (IoMT) represents a paradigm shift in the healthcare sector, enabling the interconnection of medical devices, sensors, and systems to enhance patient monitoring, diagnosis, and management. The rapid evolution of IoMT presents significant benefits to the healthcare domains. However, there is a rapid increase in distributed denial of service (DDoS) attacks on the IoMT networks due to several vulnerabilities in the IoMT-connected devices, which negatively impact patients' health and can even lead to deaths. Thus, in this paper, we aim to save lives via investigating an extreme learning machine for detecting DDoS attacks on IoMT devices. The proposed approach achieves a high accuracy at a low implementation budget. Thus, it can reduce the implementation cost of the DDoS detection system, making the model capable of executing on the fog level.

cs.CR↗

arXiv2025

A Fuzzy Supervisor Agent Design for Clinical Reasoning Assistance in a Multi-Agent Educational Clinical Scenario Simulation

Assisting medical students with clinical reasoning (CR) during clinical scenario training remains a persistent challenge in medical education. This paper presents the design and architecture of the Fuzzy Supervisor Agent (FSA), a novel component for the Multi-Agent Educational Clinical Scenario Simulation (MAECSS) platform. The FSA leverages a Fuzzy Inference System (FIS) to continuously interpret student interactions with specialized clinical agents (e.g., patient, physical exam, diagnostic, intervention) using pre-defined fuzzy rule bases for professionalism, medical relevance, ethical behavior, and contextual distraction. By analyzing student decision-making processes in real-time, the FSA is designed to deliver adaptive, context-aware feedback and provides assistance precisely when students encounter difficulties. This work focuses on the technical framework and rationale of the FSA, highlighting its potential to provide scalable, flexible, and human-like supervision in simulation-based medical education. Future work will include empirical evaluation and integration into broader educational settings. More detailed design and implementation is~\href{https://github.com/2sigmaEdTech/MAS/}{open sourced here}.

cs.CY↗

arXiv2025

LLM-as-a-Fuzzy-Judge: Fine-Tuning Large Language Models as a Clinical Evaluation Judge with Fuzzy Logic

Clinical communication skills are critical in medical education, and practicing and assessing clinical communication skills on a scale is challenging. Although LLM-powered clinical scenario simulations have shown promise in enhancing medical students' clinical practice, providing automated and scalable clinical evaluation that follows nuanced physician judgment is difficult. This paper combines fuzzy logic and Large Language Model (LLM) and proposes LLM-as-a-Fuzzy-Judge to address the challenge of aligning the automated evaluation of medical students' clinical skills with subjective physicians' preferences. LLM-as-a-Fuzzy-Judge is an approach that LLM is fine-tuned to evaluate medical students' utterances within student-AI patient conversation scripts based on human annotations from four fuzzy sets, including Professionalism, Medical Relevance, Ethical Behavior, and Contextual Distraction. The methodology of this paper started from data collection from the LLM-powered medical education system, data annotation based on multidimensional fuzzy sets, followed by prompt engineering and the supervised fine-tuning (SFT) of the pre-trained LLMs using these human annotations. The results show that the LLM-as-a-Fuzzy-Judge achieves over 80\% accuracy, with major criteria items over 90\%, effectively leveraging fuzzy logic and LLM as a solution to deliver interpretable, human-aligned assessment. This work suggests the viability of leveraging fuzzy logic and LLM to align with human preferences, advances automated evaluation in medical education, and supports more robust assessment and judgment practices. The GitHub repository of this work is available at https://github.com/2sigmaEdTech/LLMAsAJudge

cs.AI↗

arXiv2025

Mapping Trafficking Networks: A Data-Driven Approach to Disrupt Human Trafficking Post Russia-Ukraine Conflict

This study proposes a prototype for locating important individuals and financial exchanges in networks of people trafficking that have grown during the conflict between Russia and Ukraine. It focuses on the role of digital platforms, cryptocurrencies, and the dark web in facilitating these operations. The research maps trafficking networks and identifies key players and financial flows by utilizing open-source intelligence (OSINT), social network analysis (SNA), and blockchain analysis. The results show how cryptocurrencies are used for anonymous transactions and imply that upsetting central coordinators may cause wider networks to become unstable. In order to combat human trafficking, the study emphasizes the significance of real-time data sharing between international law enforcement. It also identifies future directions for the development of improved monitoring tools and cooperative platforms.

cs.CY↗

arXiv2025

Cyber Value At Risk Model for IoT Ecosystems

The Internet of Things (IoT) presents unique cybersecurity challenges due to its interconnected nature and diverse application domains. This paper explores the application of Cyber Value-at-Risk (Cy-VaR) models to assess and mitigate cybersecurity risks in IoT environments. Cy-VaR, rooted in Value at Risk principles, provides a framework to quantify the potential financial impacts of cybersecurity incidents. Initially developed to evaluate overall risk exposure across scenarios, our approach extends Cy-VaR to consider specific IoT layers: perception, network, and application. Each layer encompasses distinct functionalities and vulnerabilities, from sensor data acquisition (perception layer) to secure data transmission (network layer) and application-specific services (application layer). By calculating Cy- VaR for each layer and scenario, organizations can prioritize security investments effectively. This paper discusses methodologies and models, including scenario-based Cy-VaR and layer-specific risk assessments, emphasizing their application in enhancing IoT cybersecurity resilience.

cs.CY↗

arXiv2025

Integrating Graph Theoretical Approaches in Cybersecurity Education CSCI-RTED

As cybersecurity threats continue to evolve, the need for advanced tools to analyze and understand complex cyber environments has become increasingly critical. Graph theory offers a powerful framework for modeling relationships within cyber ecosystems, making it highly applicable to cybersecurity. This paper focuses on the development of an enriched version of the widely recognized NSL-KDD dataset, incorporating graph-theoretical concepts to enhance its practical value. The enriched dataset provides a resource for students and professionals to engage in hands-on analysis, enabling them to explore graph-based methodologies for identifying network behavior and vulnerabilities. To validate the effectiveness of this dataset, we employed IBM Auto AI, demonstrating its capability in real-world applications such as classification and threat prediction. By addressing the need for graph-theoretical datasets, this study provides a practical tool for equipping future cybersecurity professionals with the skills necessary to confront complex cyber challenges.

cs.CR↗

arXiv2024

Adapting to the AI Disruption: Reshaping the IT Landscape and Educational Paradigms

Artificial intelligence (AI) signals the beginning of a revolutionary period where technological advancement and social change interact to completely reshape economies, work paradigms, and industries worldwide. This essay addresses the opportunities and problems brought about by the AI-driven economy as it examines the effects of AI disruption on the IT sector and information technology education. By comparing the current AI revolution to previous industrial revolutions, we investigate the significant effects of AI technologies on workforce dynamics, employment, and organizational procedures. Human-centered design principles and ethical considerations become crucial requirements for the responsible development and implementation of AI systems in the face of the field's rapid advancements. IT education programs must change to meet the changing demands of the AI era and give students the skills and competencies they need to succeed in a digital world that is changing quickly. In light of AI-driven automation, we also examine the possible advantages and difficulties of moving to a shorter workweek, emphasizing chances to improve worker productivity, well-being, and work-life balance. We can build a more incslusive and sustainable future for the IT industry and beyond, enhancing human capabilities, advancing collective well-being, and fostering a society where AI serves as a force for good by embracing the opportunities presented by AI while proactively addressing its challenges.

cs.CY↗

arXiv2024

Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks

Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies.

cs.LG↗

arXiv2024

Not as Simple as It Looked: Are We Concluding for Biased Arrest Practices?

This study examines racial disparities in violent arrest outcomes, challenging conventional methods through a nuanced analysis of Cincinnati Police Department data. Acknowledging the intricate nature of racial disparity, the study categorizes explanations into types of place, types of person, and a combination of both, emphasizing the impact of neighborhood characteristics on crime distribution and police deployment. By introducing alternative scenarios, such as spuriousness, directed policing, and the geo-concentration of racial groups, the study underscores the complexity of racial disparity calculations. Employing a case study approach, the analysis of violent arrest outcomes reveals approximately 40 percent of the observed variation attributed to neighborhood-level characteristics, with concentrated disadvantage neutralizing the influence of race on arrest rates. Contrary to expectations, the study challenges the notion of unintentional racism, suggesting that neighborhood factors play a more significant role than the racial composition in explaining arrests. Policymakers are urged to focus on comprehensive community development initiatives addressing socioeconomic inequalities and support the development of robust racial disparity indices. The study calls for nuanced explorations of unintentional racism and future research addressing potential limitations, aiming to enhance understanding of the complexities surrounding racial disparities in arrests.

cs.CY↗

arXiv2024

The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on Data Breach Trends

This study examines the impact of the COVID-19 pandemic on cybersecurity and data breaches, with a specific focus on the shift toward remote work. The study identifies trends and offers insights into cybersecurity incidents by analyzing data breaches two years before and two years after the start of remote work. Data was collected from the Montana Department of Justice Data Breach database and consisted of data breaches that occurred between April 2018 and April 2022. The findings inform best practices for cybersecurity preparedness in remote work environments, aiding organizations to enhance their defenses. Although the study's data is limited to Montana, it offers valuable insights for cybersecurity professionals worldwide. As remote work continues to evolve, organizations must remain adaptable and vigilant in their cybersecurity strategies.

cs.CR↗

arXiv2024

The Pulse of Fileless Cryptojacking Attacks: Malicious PowerShell Scripts

Fileless malware predominantly relies on PowerShell scripts, leveraging the native capabilities of Windows systems to execute stealthy attacks that leave no traces on the victim's system. The effectiveness of the fileless method lies in its ability to remain operational on victim endpoints through memory execution, even if the attacks are detected, and the original malicious scripts are removed. Threat actors have increasingly utilized this technique, particularly since 2017, to conduct cryptojacking attacks. With the emergence of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries, widespread cryptocurrency mining attacks have become prevalent, often employing fileless techniques. This paper provides a comprehensive analysis of PowerShell scripts of fileless cryptojacking, dissecting the common malicious patterns based on the MITRE ATT&CK framework.

cs.CR↗

arXiv2024

Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems

In the dynamic urban landscape, where the interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This datasets is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.

cs.CV↗

arXiv2024

A Comprehensive Analytical Review on Cybercrime in West Africa

Cybercrime is a growing concern in West Africa due to the increasing use of technology and internet penetration in the region. Legal frameworks are essential for guiding the control of cybercrime. However, the implementation proves challenging for law enforcement agencies due to the absence of a dedicated and effective regional institutional follow-up mechanism. This study conducted a systematic literature review focusing on West Africa's prevalence of cybercrime, governing policies, regulations, and methodologies for combating cybercrime. West-Africa countries face significant cybercrime challenges, exacerbated by inadequate resources and a dearth of security experts. This study pinpoints potential cybercrime prevention strategies, such as leveraging the Triage framework and broadening research to cover pivotal areas like cyber aggression and cyberbullying. Our research findings highlight the urgency for policymakers and law enforcement agencies to devise more efficient prevention strategies and policies. Overall, this study provides invaluable insights into the state of cybercrime in West Africa to guide the formulation of potent prevention and intervention strategies.

cs.CY↗

arXiv2024

CautionSuicide: A Deep Learning Based Approach for Detecting Suicidal Ideation in Real Time Chatbot Conversation

Suicide is recognized as one of the most serious concerns in the modern society. Suicide causes tragedy that affects countries, communities, and families. There are many factors that lead to suicidal ideations. Early detection of suicidal ideations can help to prevent suicide occurrence by providing the victim with the required professional support, especially when the victim does not recognize the danger of having suicidal ideations. As technology usage has increased, people share and express their ideations digitally via social media, chatbots, and other digital platforms. In this paper, we proposed a novel, simple deep learning-based model to detect suicidal ideations in digital content, mainly focusing on chatbots as the primary data source. In addition, we provide a framework that employs the proposed suicide detection integration with a chatbot-based support system.

cs.HC↗