Andrii Shalaginov is a Professor in Cyber Security at Kristiania University of Applied Sciences, School of Economics, Innovation and Technology. He leads the Smart Security Lab with a research focus on applying artificial intelligence for cybersecurity, detection of computer viruses, network attacks and protection of Internet of Things devices. Dr. Shalaginov holds a PhD in Information Security from the Norwegian University of Science and Technology (NTNU), where he worked with digital forensics at the Center for Cyber and Information Security (CCIS). His research spans multiple critical areas of modern cybersecurity with a particular emphasis on Internet of Things security , machine learning applications for threat detection, and digital forensics . He has developed expertise in intelligent malware detection systems, network security for smart environments, and forensic analysis of IoT devices. His work bridges theoretical computer science with practical security implementations for real-world systems. Analysis of his recent publication trends (2022-2025) reveals a strong focus on AI-enabled security solutions for IoT ecosystems, vulnerability detection frameworks, and resource-efficient security mechanisms for constrained devices. His research increasingly addresses the intersection of machine learning and practical security implementations, with growing attention to energy efficiency in security protocols and international data space security frameworks. Dr. Shalaginov has received professional recognition through: Nomination as Norwegian representative in EU COST Action CA17124 "DigForAsp" Nomination as Norwegian representative in EU COST Action CA22104 "BEiNG-WISE" Membership in the "Impact of Technology" Expert Group at the European Union Intellectual Property Office Observatory As head of the Smart Security Lab, Dr. Shalaginov oversees research projects focused on developing practical security solutions for emerging technologies. His GitHub activity demonstrates active development of open-source tools for malware analysis across multiple platforms (Linux, Windows, IoT) and implementation of machine learning approaches for security applications. His research group appears to focus on bridging the gap between theoretical security models and practical implementations for real-world systems.








