The Cyber Security research area at our department explores the protection of complex data-driven and cyber-physical systems, bridging domains of infrastructure resilience, applied security data science, and trustworthy AI. The group focuses on emerging and high-impact problems at the intersection of critical infrastructure, machine learning for security, anomaly-detection in industrial control systems, and cyber-education.

Key themes of our work include:

• Anomaly detection and root-cause identification in industrial control systems (ICS).
• Security in critical infrastructure and distributed systems, capturing how data engineering and cyber-physical interdependencies influence attack surfaces and resilience.
• Machine learning and data-science methods applied to cybersecurity problems, with emphasis on explainability, resource-efficiency and operational contexts.
• Cybersecurity education, curriculum development and workforce readiness in Europe, addressing the skills gap in cybersecurity via novel programmes and partnerships.
• Trust, consensus and applied blockchains in archival or networked settings, exploring how blockchain and consensus mechanisms can underpin trustworthy data ecosystems.
• Artificial intelligence (AI) security with a specific focus on privacy and data protection, federated learning, differential privacy and synthetic data generation algorithms.

By combining rigorous data science techniques and domain knowledge gained in different critical infrastructure sectors, our group advances both theoretical foundations and applied solutions. We collaborate with industry, public-sector actors and academic partners to translate our research into operational impact. We welcome motivated students, thesis candidates and external collaborators to engage with our research agenda — whether in anomaly-detection pipelines, secure system design, cyber-education initiatives, or the secure application of AI.

Contact us!

Please send an email to Imre Lendák for further inquiries.