Postdoctoral ResearcherDepartment of Information Security and Communication Technology Faculty of Information Technology and Electrical Engineering
Background and activities
Dr. Andrii Shalaginov is working as a Postdoctoral Researcher in Digital Forensics at the Department of Information Security and Communication Technology (IIK), he is a member of NTNU Digital Forensics group and NTNU Malware Lab. Andrii's expertise lies in developement of new method to protect against cybersecurity threats using advanced data analytics. The main aspect is intelligent processing of data pieces that further can be used for building AI models to defend intrustructure as well as preserving digital evidences.
Current Research Interensts
- Malware Anlaysis
- Digital Forensics
- Big Data Analytics
- Machine Learning
- Internet of Things
Andrii obtained his PhD in Information Securtiy in 2018 from NTNU. This research project included developement and proof-of-concept demonstration of advanced Neuro-Fuzzy method for Big Data problems in Digital Forensics applications. One of the contributions received aware from AI Journal. By today, Andrii holds his MSc degree in Information Security (Digital Forensics track) from Gjøvik University College (Norway), MSc in System Design from Kyiv Polytechnic Institute (Ukraine) and BSc in Information Technology from Kyiv Polytechnic Institute (Ukraine). Moreover, Andrii experienced in system architecture and software engineering. In 2010-2011 he worked with Samsung R&D Center on Human-Computer Interaction Porjects on embedded devices for Android platform.
- Digital forensics: evidence analysis via intelligent systems and practices, COST Action CA17124, nominated representative from Norway, WP7 ECR Vice-Leader, 2018-current
- ArsForensica, NTNU; WP2 demonstrator, 2015-current
- Malware on copyright infinding websites, UNICRI/EUIPO; security consultant, 2017.
- SuPLight, NTNU; WP3 editor, WP6 software developer and demonstrator, 2011-2014.
- Large-Scale Multinomial Malware Classification, NTNU, Project Leader, 2015-current.
- Hansken, NTNU, system architect, 2016-current
- IMT4133 - Data Science for Security and Forensics (course responsible)
- IMT4114 - Introduction to Digital Forensics (course responsible)
Selected Invited Talks
- Future Smart Cities Policing: Opportunities and Challenges, Interpol, Singapore, 2018.
- Malware on selected suspected copyright infringing websites, European Union Agency for Law Enforcement Training (CEPOL), 2017.
- Machine Learning-Aided Malware Analysis, NorCERT Security Forum - NTNU Malware Forum, 2017.
- Member of "Impact of Technology Expert Group", Observatory Expert Groups, European Union Intellectual Property Office, 2019.
- Andrii is part of NTNU team that won 1st place at Interpol Thinkathon on Future Policing in Smart Cities (PolitiForum) (2018)
- Chair of "International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention" 2017, 2018
- Co-chair of "NTNU Malware Forum" (2017-current)
- COINS steering comittee member and student representative (2015-2017)
- Member of Machine Intelligence Research Labs (MIR Labs) (2018-current)
- Member of International Neural Network Society (INNS) (2017-current)
- Member of Institute of Electrical and Electronics Engineers (IEEE) (2018-current).
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2020) Intelligent mobile malware detection using permission requests and API calls. Future generations computer systems. vol. 107.
- (2020) Decentralized Self-Enforcing Trust Management System for Social Internet of Things. IEEE Internet of Things Journal. vol. 7 (4).
- (2020) Study of Blacklisted Malicious Domains from a Microsoft Windows End-user Perspective: Is It Safe Behind the Wall?. Norsk Informasjonssikkerhetskonferanse (NISK).
- (2020) PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting. Future Internet. vol. 12 (4).
- (2020) RESPOnSE—A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments. Sensors. vol. 20 (10).
- (2020) Smart Policing for a Smart World Opportunities, Challenges and Way Forward. Advances in Intelligent Systems and Computing.
- (2019) Predicting likelihood of legitimate data loss in email DLP. Future generations computer systems.
- (2018) Comparing Open Source Search Engine Functionality, Efficiency and Effectiveness with Respect to Digital Forensic Search. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 11.
- (2017) Fuzzy logic model for digital forensics: A trade-off between accuracy, complexity and interpretability. IJCAI International Joint Conference on Artificial Intelligence.
- (2017) Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness. Applied Soft Computing. vol. 52.
- (2016) Data-driven Approach to Information Sharing using Data Fusion and Machine Learning for Intrusion Detection. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 2016.
- (2016) Memory access patterns for malware detection. Norsk Informasjonssikkerhetskonferanse (NISK). vol. 2016.
- (2016) Intelligent generation of fuzzy rules for network firewalls based on the analysis of large-scale network traffic dumps. International Journal of Hybrid Intelligent Systems. vol. 13 (3-4).
- (2016) Multinomial classification of web attacks using improved fuzzy rules learning by Neuro-Fuzzy. International Journal of Hybrid Intelligent Systems. vol. 13 (1).
- (2016) Malware Beaconing Detection by Mining Large-scale DNS Logs for Targeted Attack Identification. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering. vol. 10 (4).
- (2016) Cyber security risk assessment of a DDoS attack. Lecture Notes in Computer Science (LNCS). vol. 9866.
- (2021) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer. 2021. ISBN 978-3-030-62581-8.
- (2017) IEEE Big Data 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2017. IEEE. 2017. ISBN 978-1-5386-2715-0.
Part of book/report
- (2021) Review of the Malware Categorization in the Era of Changing Cybethreats Landscape: Common Approaches, Challenges and Future Needs. Malware Analysis Using Artificial Intelligence and Deep Learning.
- (2021) A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families Through Deep Neural Networks. Malware Analysis Using Artificial Intelligence and Deep Learning.