Rafal Kozik

Rafal Kozik is an active Associate Professor at Lublin University of Technology's Faculty of Electrical Engineering and Computer Science, Department of Computer Science. With over 200 publications spanning from 2008 to 2025, he has established himself as a prominent researcher in cybersecurity, artificial intelligence, and explainable AI systems. His work demonstrates strong collaboration with researchers including Michal Choras, Marek Pawlicki, and Aleksandra Pawlicka, with whom he has co-authored numerous significant publications.

Dr. Kozik's research focuses primarily on cybersecurity applications of artificial intelligence, with particular emphasis on explainable AI (xAI) systems, fake news detection, network intrusion detection, and financial cybercrime analysis. His work bridges theoretical AI advancements with practical security applications, addressing critical challenges in making AI systems both effective and transparent in security contexts. He has made significant contributions to understanding the dual nature of explainability in security systems - how it can both enhance trust and potentially create new vulnerabilities.

His recent publication trends show an increasing focus on the intersection of AI explainability and security, with numerous high-impact papers in 2023-2025 examining how explainable AI can be leveraged for security applications while also addressing how these same techniques might be weaponized against security systems. His work spans multiple domains including cybersecurity, disinformation detection, financial crime analysis, and even environmental applications like wildfire prevention systems.

Dr. Kozik has been actively involved in major European cybersecurity initiatives, serving as an editor for ESORICS 2024 (European Symposium on Research in Computer Security) proceedings. His research has been published in top venues including IEEE Transactions, Computers & Security, Neurocomputing, and major conference proceedings like ARES and DSAA.

His work demonstrates strong practical application focus, with numerous projects addressing real-world security challenges through innovative AI approaches. He has contributed significantly to the development of tools and frameworks for network security, fake news detection, and financial transaction monitoring that balance effectiveness with explainability requirements.