Andrii Shalaginov is a Postdoctoral Researcher in Information Security / Digital Forensics at the Norwegian University of Science and Technology (NTNU Digital Forensic Group / NTNU Malwarelab). His primary expertise is in static and dynamic malware analysis, development of machine learning-aided intelligent computer viruses detection models and similarity-based categorization of malware types and families. By now, Andrii received his PhD in Information Security (2018), 2nd Master Degree in Digital Forensics from the Gjøvik University College in 2013 and also holds BSc (2009) and 1st MSc (2011) degrees in System Designing from the National Technical University of Ukraine “Kyiv Polytechnic Institute” - Department of Computer Aided Design. Main research interest includes intelligent malware analysis, Internet of Things Security / Forensic, Soft Computing and Big Data Analytics.

- Shalaginov, Andrii, Jan William Johnsen, and Katrin Franke. "Cyber crime investigations in the era of big data." Big Data (Big Data), 2017 IEEE International Conference on. IEEE, 2017.

- Shalaginov, Andrii, and Katrin Franke. "A deep neuro-fuzzy method for multi-label malware classification and fuzzy rules extraction." Computational Intelligence (SSCI), 2017 IEEE Symposium Series on. IEEE, 2017.

- Shalaginov, Andrii, and Katrin Franke. "Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness." Applied Soft Computing 52 (2017): 359-375.

- Shalaginov, Andrii. "Soft computing and hybrid intelligence for decision support in forensics science." Intelligence and Security Informatics (ISI), 2016 IEEE Conference on. IEEE, 2016.

- Shalaginov, Andrii, and Katrin Franke. "A new method of fuzzy patches construction in Neuro-Fuzzy for malware detection." IFSA-EUSFLAT. 2015.

- The 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention at the IEEE Big Data 2017, chair

- NTNU / NorCERT Malware Forum 2017, keynote speaker

- NTNU / NorCERT Malware Forum 2018, co-chair