My research focuses on applied cybersecurity, artificial intelligence security, secure software engineering, and secure systems design. I am interested in developing practical solutions to emerging cybersecurity challenges and bridging the gap between academic research and real-world implementation.

My work combines cybersecurity, software engineering, and artificial intelligence to address issues related to digital trust, software security, and the resilience of modern computing systems. Through research, teaching, and industry collaboration, I seek to develop methods and technologies that improve the security and reliability of digital infrastructure.

Research Resources

My research artifacts, software, datasets, and publications are publicly available through GitHub and Zenodo.

Google Scholar: https://scholar.google.com/citations?user=v4S2JYEAAAAJ

Orcid: https://orcid.org/0009-0005-7470-9404

Github: https://github.com/dursunoglu/

Research Gate: https://www.researchgate.net/profile/Halil-Dursunoglu

Artificial Intelligence Security and Deepfake Detection

The rapid adoption of artificial intelligence has created new challenges related to digital trust, identity verification, and information integrity. My current research investigates AI-generated media, deepfake detection, and synchronization-based approaches for identifying manipulated audio and video content.

Recent work includes the publication:

Audio-Visual Synchronization Analysis for Deepfake Detection: A Comprehensive Review

International Journal of Computer Techniques (2026)

I also contributed as a subject matter expert to RSA Conference (RSAC) on AI impersonation threats and synchronization-based approaches for improving digital trust.

Secure Software Engineering and Software Security

Software vulnerabilities remain one of the most significant cybersecurity risks facing organizations today. My research explores secure software development practices, vulnerability analysis, secure-by-design methodologies, and the security implications of AI-assisted software development.

Current projects include:

• Security Weaknesses in LLM-Generated Source Code: An Empirical Vulnerability Analysis of Iterative AI-Assisted Development
• SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

These projects investigate how software vulnerabilities emerge, evolve, and can be detected through automated analysis techniques.

Secure Systems and Cybersecurity Education

A significant portion of my work focuses on preparing the next generation of cybersecurity professionals through experiential learning and applied security education.

My research and educational activities include:

• Secure systems design
• Network security
• Web application security
• Secure programming
• Cybersecurity workforce development
• Project-based cybersecurity education

I have redesigned cybersecurity-focused curricula, developed hands-on security laboratories, and mentored student teams in secure software development, startup-oriented projects, and applied cybersecurity initiatives.

Human Activity Recognition and Intelligent Systems

My earlier research investigated wearable sensors, activity monitoring, and intelligent classification systems for human activity recognition. This work explored machine learning techniques for recognizing complex activities using sensor-based data and contributed to research on intelligent monitoring systems.

Research Impact

My research aims to contribute to:

• Artificial intelligence security
• Digital trust and media authenticity
• Secure software development
• Cybersecurity workforce development
• Secure systems engineering
• Practical cybersecurity education

By combining academic research, industry experience, and cybersecurity education, I seek to develop solutions that address emerging security challenges while preparing future professionals to secure increasingly complex digital systems.