Andrii Shalaginov

Andrii Shalaginov is an Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU). His research focuses on cybersecurity, IoT security, machine learning for threat detection, malware analysis, and digital forensics. He has contributed to numerous publications in top-tier journals and conferences, addressing challenges in smart IoT ecosystems, vulnerability detection, and energy-efficient security solutions.

Key research interests include:

• Cybersecurity frameworks for international data spaces
• Resource-efficient IoT threat detection mechanisms
• Machine learning applications in malware classification and forensic readiness
• Energy optimization in AIoT networks

His work spans academic collaboration, with notable contributions to frameworks like IoTvulCode for vulnerability detection and MEML for edge-based attack detection. He frequently publishes in venues such as IEEE Access, Future Generation Computer Systems, and Sensors.

Notable projects include:

• Development of distributed deep neural network-based middleware for IoT cyberattack detection
• Design of hybrid IoT cyber ranges for security training
• Analysis of blacklisted malicious domains in Windows environments

He actively participates in academic workshops and conferences, emphasizing the integration of AI and big data analytics for cybersecurity challenges.