VORN Naro
Academic Background
- Current: Master's Degree in Big Data Convergence (Integrated Student), Chungbuk National University, South Korea (September 2024 - Present)
- Bachelor's Degree: Computer Science, Royal University of Phnom Penh, Cambodia (2023)
Professional Experience
Brings a unique combination of backend development expertise and cybersecurity research experience, providing a strong foundation for AI-driven security applications.
- 3 years of experience in Backend Development using Java
- 1 year of experience as a Cybersecurity Researcher
- Focus on penetration testing and system security assessment
- Current: Researcher at AI Convergence Lab, Chungbuk National University
Research Focus
His work explores the intersection of multimodal AI, large language models (LLMs), and cybersecurity applications. This unique combination aims to develop intelligent security systems that leverage AI for enhanced digital protection.
- Multimodal Learning (MLLMs): Integrating multiple data types for comprehensive AI understanding
- AI-driven Cybersecurity Systems: Applying AI to detect and prevent security threats
- Applied Artificial Intelligence: Real-world problem-solving with AI
- Large Language Models: Leveraging LLMs for security applications
- Intelligent Automation: Developing AI-driven systems for process automation
Publications
Evaluating DNSSEC Against DNS Spoofing/MITM Attack in Virtualized Networks
Authors: Naro Vorn, Tae-Kyung Kim
Journal: International Journal of Contents (IJoC), Vol. 21, No. 3 - View Paper
AI-Scientist Web-Based System for Streamlined Scientific Paper Generation
Authors: Naro Vorn, Sokheang Chan, Vungsovanreach Kong, Tae-Kyung Kim
Conference: BIGDAS Conference 2024 - View Conference
Research Projects
IntelliScrape: An AI-assisted Web Scraping Module Generator
An AI-assisted module development system designed to automate COOCON's scraping module creation process. The system leverages artificial intelligence, network traffic analysis, and browser automation technologies to reduce manual effort, minimize human error, and accelerate development lifecycle.
Key Contributions:
- Backend Architecture & Pipeline Design: Architected and implemented the complete project pipeline for the request transmitter component using Python and FastAPI, ensuring scalable and efficient request processing
- Exception Handling & Validation: Developed robust exception handling mechanisms with iSASType engine validation, ensuring system reliability and data integrity throughout the scraping workflow
- JavaScript Analysis & Module Generation: Conducted in-depth analysis and tracing of external JavaScript functions to facilitate intelligent and accurate dynamic module generation, bridging frontend-backend integration challenges
- Documentation & Visualization: Prepared comprehensive figures, diagrams, and video demonstrations showcasing system architecture and functionality for research dissemination and stakeholder communication
- Quality Assurance & Testing: Conducted extensive validation and testing procedures to ensure project implementation meets performance and reliability standards
- Technical Documentation: Contributed to the preparation of detailed project documentation, including technical specifications, API documentation, and implementation guides
Technologies: Python, FastAPI, JavaScript Analysis, Large Language Models, Browser Automation, Network Traffic Analysis, Exception Handling
Technical Expertise
- Backend Development: 3 years with Java, building robust server-side systems
- Cybersecurity: Penetration testing, vulnerability assessment, system security
- Big Data Technologies: Processing and analyzing large-scale datasets
- Multimodal AI: Working with diverse data types in AI systems
- Large Language Models: Integration and application in security contexts
Cybersecurity Background
With one year of focused cybersecurity research experience, Vorn Naro has developed expertise in penetration testing and system security assessment. This background provides valuable insights into security vulnerabilities and defense mechanisms, which he now integrates with AI technologies to create more intelligent and proactive security solutions.
Research Goals
Aims to develop scalable and intelligent AI systems that enhance digital security and bridge the gap between academic research and industry innovation. His goal is to create AI-powered security solutions that are both theoretically sound and practically deployable.
Unique Perspective
The combination of backend development experience, cybersecurity research, and AI expertise provides Vorn Naro with a unique perspective on building secure, scalable AI systems. He understands both the technical implementation challenges and the security considerations necessary for production AI systems.
Current Research at AICLab
- Exploring multimodal AI for cybersecurity applications
- Developing LLM-based security analysis tools
- Investigating AI-driven threat detection and prevention
- Building scalable security systems using big data analytics
- Bridging AI research with practical cybersecurity needs
Vision
Envisions a future where AI and cybersecurity are deeply integrated, creating intelligent systems that can proactively identify and respond to security threats. His work aims to make digital environments safer through the application of advanced AI technologies, while ensuring these systems are scalable and practical for real-world deployment.