Publications & Research
Academic contributions: AI application in cybersecurity, critical infrastructure protection.
Conference Publications
From OSINT Insights to AI-based Protection: Enhancing Cybersecurity Resilience in Power Distribution Systems
Proceedings of the 60th International Universities Power Engineering Conference (UPEC), IEEE, 2025
Abstract
Research: comprehensive approach. Enhance cybersecurity resilience in power distribution systems. Integrates OSINT methodologies, AI-based protection. Addresses critical vulnerabilities in modern power infrastructure. Proposes novel AI-driven solutions: real-time threat detection, response.
Key Contributions
- OSINT-based threat intelligence gathering framework development.
- AI-driven anomaly detection algorithms for power systems implementation.
- Machine learning models for predictive cybersecurity analysis integration.
- Protection mechanisms evaluation in critical infrastructure environments.
Technologies & Methods
Customer-Level Aggregation: The Imperative for Reliable Non-Technical Loss Detection Amidst Row-Level Analysis Pitfalls
Proceedings of the 60th International Universities Power Engineering Conference (UPEC), IEEE, 2025
Abstract
Paper addresses critical challenges: non-technical loss detection in power distribution systems. Proposes customer-level aggregation methods. Demonstrates significant improvements: detection accuracy. Avoids pitfalls: row-level analysis approaches in electricity theft identification.
Key Contributions
- Novel customer-level aggregation methodology for electricity theft detection.
- Comprehensive analysis: row-level analysis limitations, biases.
- Robust machine learning pipeline development: anomaly detection.
- Empirical validation: real-world power distribution data.
Technologies & Methods
Research Areas
Cybersecurity
Focus: AI-driven cybersecurity solutions for critical infrastructure. Includes threat detection, vulnerability assessment, protective mechanisms for power distribution systems.
Machine Learning
Develops advanced machine learning models: anomaly detection, predictive analytics, pattern recognition in complex systems, large-scale data environments.
Critical Infrastructure
Research: AI and data science methods application. Enhance security, reliability, efficiency of critical infrastructure systems.
Academic Background
Kadir Has University Research Center on Cybersecurity and Critical Infrastructure Protection
Research Position: Software Developer & Data Scientist (Oct 2023 - June 2025)
Contributes to cutting-edge research: cybersecurity, critical infrastructure protection. Develops machine learning models, data processing pipelines, security monitoring systems.
Research Focus:
- AI-driven cybersecurity solutions for power systems.
- Anomaly detection in critical infrastructure.
- OSINT methodologies for threat intelligence.
- Machine learning applications in energy systems.
Collaboration & Future Research
Interested in research projects. Bridges academic theory, practical applications: cybersecurity, machine learning, critical infrastructure protection.