Andrii Shalaginov

Vitenskapelige publikasjoner

• Jensen, Markus Wiik, Ren, Huamin & Shalaginov, Andrii (2024). Day-ahead Electricity Price Forecasting of Elspot markets in Norway. International Academy, Research and Industry Association (IARIA). ISBN 978-1-68558-139-8.
• Bhandari, Guru, Lyth, Andreas, Shalaginov, Andrii & Grønli, Tor-Morten (2023). Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach. Electronics. ISSN 2079-9292. 12(2) doi:
  10.3390/electronics12020298
• Balto, Karl Edvard Eriksen, Yamin, Muhammad Mudassar, Shalaginov, Andrii & Katt, Basel (2023). Hybrid IoT Cyber Range. Sensors. ISSN 1424-8220. 23(6) doi:
  10.3390/s23063071
• Bouzidi, Mohammed, Gupta, Nishu, Alaya Cheikh, Faouzi, Shalaginov, Andrii & Derawi, Mohammad (2022). A Novel Architectural Framework on IoT Ecosystem, Security Aspects and Mechanisms: A Comprehensive Survey. IEEE Access. ISSN 2169-3536. 10 s 101362-101384. doi:
  10.1109/ACCESS.2022.3207472
• Cvitić, Ivan, Peraković, Dragan, Periša, Marko, Jevremović, Aleksandar & Shalaginov, Andrii (2022). An Overview of Smart Home IoT Trends and related Cybersecurity Challenges. Mobile Networks and Applications. ISSN 1383-469X. doi:
  10.1007/s11036-022-02055-w
• Shalaginov, Andrii, Stamp, Mark & Alazab, Mamoun (2021). Malware Analysis Using Artificial Intelligence and Deep Learning. Springer. ISBN 978-3-030-62581-8.
  FULLTEKST
• Shalaginov, Andrii & Øverlier, Lasse (2021). A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families Through Deep Neural Networks. I Shalaginov, Andrii, Stamp, Mark & Alazab, Mamoun (red.) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer. ISBN 978-3-030-62581-8. s 437-453. doi:
  10.1007/978-3-030-62582-5_17
• Shalaginov, Andrii, Dyrkolbotn, Geir Olav & Alazab, Mamoun (2021). Review of the Malware Categorization in the Era of Changing Cybethreats Landscape: Common Approaches, Challenges and Future Needs. I Shalaginov, Andrii, Stamp, Mark & Alazab, Mamoun (red.) Malware Analysis Using Artificial Intelligence and Deep Learning. Springer. ISBN 978-3-030-62581-8. s 71-96. doi:
  10.1007/978-3-030-62582-5_3
• Jensen, Øyvind, Shalaginov, Andrii & Dyrkolbotn, Geir Olav (2021). Study of Blacklisted Malicious Domains from a Microsoft Windows End-user Perspective: Is It Safe Behind the Wall? Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. (3)
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• Awuson-David, Kenny, Al-Hadhrami, Tawfik, Alazab, Mamoun, Shah, Nazaraf & Shalaginov, Andrii (2021). BCFL logging: An approach to acquire and preserve admissible digital forensics evidence in cloud ecosystem. Future Generation Computer Systems. ISSN 0167-739X. 122 s 1-13. doi:
  10.1016/j.future.2021.03.001
• Guo, Zhiwei, Tang, Lianggui, Guo, Tan, Yu, Keping, Alazab, Mamoun & Shalaginov, Andrii (2021). Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Future Generation Computer Systems. ISSN 0167-739X. 117 s 205-218. doi:
  10.1016/j.future.2020.11.028
• Shalaginov, Andrii & Azad, Muhammad Ajmal (2021). Securing resource-constrained iot nodes: Towards intelligent microcontroller-based attack detection in distributed smart applications. Future Internet. ISSN 1999-5903. 13(11) doi:
  10.3390/fi13110272
• Shalaginov, Andrii & Grønli, Tor-Morten (2021). Securing Smart Future: Cyber Threats and Intelligent Means to Respond. I Chen, Yixin, Ludwig, Heiko, Tu, Yicheng, Fayyad, Usama, Zhu, Xingquan, Hu, Xiaohua, Byna, Surendra, Liu, Xiong, Zhang, Jianping, Pan, Shirui, Papalexakis, Vagelis, Wang, Jianwu, Cuzzocrea, Alfredo & Ordóñez, Carlos (red.) 2021 IEEE International Conference on Big Data. IEEE Press. ISBN 978-1-6654-3902-2. s 2560-2564. doi:
  10.1109/BigData52589.2021.9671703
• Yamin, Muhammad Mudassar, Shalaginov, Andrii & Katt, Basel (2020). Smart Policing for a Smart World Opportunities, Challenges and Way Forward. Advances in Intelligent Systems and Computing. ISSN 2194-5357. s 532-549. doi:
  10.1007/978-3-030-39445-5_39
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• Alazab, Moutaz, Alazab, Mamoun, Shalaginov, Andrii, Mesleh, Abdelwadood & Awajan, Albara (2020). Intelligent mobile malware detection using permission requests and API calls. Future Generation Computer Systems. ISSN 0167-739X. 107 s 509-521. doi:
  10.1016/j.future.2020.02.002
• Michailidou, Christina, Gkioulos, Vasileios, Shalaginov, Andrii, Rizos, Athanasios & Saracino, Andrea (2020). RESPOnSE—A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments . Sensors. ISSN 1424-8220. 20(10) doi:
  10.3390/s20102960
• Azad, Muhammad Ajmal, Bag, Samiran, Feng, Hao & Shalaginov, Andrii (2020). Decentralized Self-Enforcing Trust Management System for Social Internet of Things. IEEE Internet of Things Journal. ISSN 2327-4662. 7(4) s 2690-2703. doi:
  10.1109/JIOT.2019.2962282
• Kumar, Ajit, Agarwal, Vinti, Kumar Shandilya, Shishir, Shalaginov, Andrii, Upadhyay, Saket & Yadav, Bhawna (2020). PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting. Future Internet. ISSN 1999-5903. 12(4) doi:
  10.3390/fi12040066
• Shalaginov, Andrii, Iqbal, Asif & Olegård, Johannes (2020). IoT Digital Forensics Readiness in the Edge: A Roadmap for Acquiring Digital Evidences from Intelligent Smart Applications. I Katangur, Ajay, Lin, Shih-Chun, Wei, Jinpeng, Yang, Shuhui & Zhang, Liang-Jie (red.) Edge Computing – EDGE 2020. Springer. ISBN 978-3-030-59823-5. doi:
  10.1007/978-3-030-59824-2_1
• Gunleifsen, Håkon, Gkioulos, Vasileios, Wangen, Gaute, Shalaginov, Andrii, Kianpour, Mazaher & Abomhara, Mohamed Ali Saleh (2019). Cybersecurity Awareness and Culture in Rural Norway. I Furnell, Steven & Clarke, Nathan (red.) Thirteenth International Symposium on Human Aspects of Information Security & Assurance (HAISA 2019). University of Plymouth Press. ISBN 978-0244-19096-5. s 110-121.
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• Shalaginov, Andrii, Semeniuta, Oleksandr & Alazab, Mamoun (2019). MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices. I Johnson, Kenneth & Spillner, Josef (red.) Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion. Association for Computing Machinery (ACM). ISBN 978-1-4503-7044-8. s 123-128. doi:
  10.1145/3368235.3368876
• Faiz, Mohamed Falah, Arshad, Junaid, Alazab, Mamoun & Shalaginov, Andrii (2019). Predicting likelihood of legitimate data loss in email DLP. Future Generation Computer Systems. ISSN 0167-739X. doi:
  10.1016/j.future.2019.11.004
• Kumar, Ajit, Agarwal, Vinti, Shandilya, Shishir K., Shalaginov, Andrii, Upadhyay, Saket & Yadav, Bhawna (2019). PACE: Platform for Android Malware Classification and Performance Evaluation. I IEEE, . (red.) 2019 IEEE International Conference on Big Data (Big Data). IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-7281-0858-2.
• Shalaginov, Andrii (2018). Advancing Neuro-Fuzzy Algorithm for Automated Classification in Large-scale Forensic and Cybercrime Investigations: Adaptive Machine Learning for Big Data Forensic. Norges teknisk-naturvitenskapelige universitet. ISBN 978-82-326-2906-0. 2018(57)
• Shalaginov, Andrii, Banin, Sergii, Dehghantanha, Ali & Franke, Katrin (2018). Machine Learning Aided Static Malware Analysis: A Survey and Tutorial. I Dehghantanha, Ali, Conti, Mauro & Dargahi, Tooska (red.) Cyber Threat Intelligence. Springer. ISBN 978-3-319-73951-9. s 7-45. doi:
  10.1007/978-3-319-73951-9_2
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• Iqbal, Asif, Mahmood, Farhan, Shalaginov, Andrii & Ekstedt, Mathias (2018). Identification of Attack-based Digital Forensic Evidences for WAMPAC Systems. I Abe, Naoki, Liu, Huan, Hu, Xiaohua, Ahmed, Nesreen, Qiao, Mu, Song, Yang, Kossmann, Donald, Liu, Bing, Lee, Kisung, Tang, Jiliang, He, Jingrui & Saltz, Jeffrey (red.) 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10-13 Dec. 2018. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5386-5035-6. s 3079-3087. doi:
  10.1109/BigData.2018.8622550
• Iqbal, Asif, Shalaginov, Andrii & Mahmood, Farhan (2018). Intelligent analysis of digital evidences in large-scale logs in power systems attributed to the attacks. I Abe, Naoki, Liu, Huan, Hu, Xiaohua, Ahmed, Nesreen, Qiao, Mu, Song, Yang, Kossmann, Donald, Liu, Bing, Lee, Kisung, Tang, Jiliang, He, Jingrui & Saltz, Jeffrey (red.) 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10-13 Dec. 2018. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5386-5035-6. s 3088-3093. doi:
  10.1109/BigData.2018.8622220
• Hansen, Joachim, Porter, Kyle, Shalaginov, Andrii & Franke, Katrin (2018). Comparing Open Source Search Engine Functionality, Efficiency and Effectiveness with Respect to Digital Forensic Search . Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. 11
  FULLTEKST
• Shalaginov, Andrii & Franke, Katrin (2017). Big data analytics by automated generation of fuzzy rules for Network Forensics Readiness. Applied Soft Computing. ISSN 1568-4946. 52 s 359-375. doi:
  10.1016/j.asoc.2016.10.029
• Shalaginov, Andrii (2017). Dynamic feature-based expansion of fuzzy sets in Neuro-Fuzzy for proactive malware detection. I IEEE, . (red.) 2017 20th International Conference on Information Fusion. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-0-9964-5270-0. s 1-8. doi:
  10.23919/ICIF.2017.8009812
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• Shalaginov, Andrii (2017). Evolutionary optimization of on-line multilayer perceptron for similarity-based access control. I Choe, Yoonsuck (red.) 2017 International Joint Conference on Neural Networks (IJCNN). IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5090-6182-2. s 823-830. doi:
  10.1109/IJCNN.2017.7965937
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• Shalaginov, Andrii (2017). Fuzzy logic model for digital forensics: A trade-off between accuracy, complexity and interpretability. IJCAI International Joint Conference on Artificial Intelligence. ISSN 1045-0823. s 5207-5208. doi:
  10.24963/ijcai.2017/763
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• Shalaginov, Andrii, Franke, Katrin & Johnsen, Jan William (2017). IEEE Big Data 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2017. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5386-2715-0.
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• Shalaginov, Andrii, Johnsen, Jan William & Franke, Katrin (2017). Cyber crime investigations in the era of big data. I Shalaginov, Andrii, Franke, Katrin & Johnsen, Jan William (red.) IEEE Big Data 1st International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2017. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5386-2715-0. s 3672-3676. doi:
  10.1109/BigData.2017.8258362
• Shalaginov, Andrii & Franke, Katrin (2017). A Deep Neuro-Fuzzy method for multi-label malware classification and fuzzy rules extraction. I Bonissone, Piero & Fogel, David (red.) 2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5386-2726-6. s 533-540. doi:
  10.1109/SSCI.2017.8280788
• Wangen, Gaute & Shalaginov, Andrii (2016). Quantitative Risk, Statistical Methods and the Four Quadrants for Information Security. I Lambrinoudakis, Costas & Gabillon, Alban (red.) Risks and Security of Internet and Systems: 10th International Conference, CRiSIS 2015, Mytilene, Lesbos Island, Greece, July 20-22, 2015, Revised Selected Papers. Springer. ISBN 978-3-319-31811-0. s 127-143. doi:
  10.1007/978-3-319-31811-0_8
• Shalaginov, Andrii & Franke, Katrin (2016). Multinomial classification of web attacks using improved fuzzy rules learning by Neuro-Fuzzy. International Journal of Hybrid Intelligent Systems. ISSN 1448-5869. 13(1) s 15-26. doi:
  10.3233/HIS-160221
• Shalaginov, Andrii, Franke, Katrin & Huang, Xiongwei (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. ISSN 2010-376X. 10(4) s 617-629.
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• Shalaginov, Andrii, Grini, Lars Strande & Franke, Katrin (2016). Understanding Neuro-Fuzzy on a Class of Multinomial Malware Detection Problems. I Estevez, Pablo A. (red.) IEEE International Joint Conference on Neural Networks (IJCNN). Research Publishing Services. ISBN 978-1-5090-0619-9. s 684-691. doi:
  10.1109/IJCNN.2016.7727266
• Shalaginov, Andrii (2016). Soft Computing and Hybrid Intelligence for Decision Support in Forensics Science. I Wang, Alan (red.) IEEE International Conference on Intelligence and Security Informatics: Cybersecurity and Big Data. IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-5090-3865-7. s 304-306. doi:
  10.1109/ISI.2016.7745495
• Wangen, Gaute, Shalaginov, Andrii & Hallstensen, Christoffer V (2016). Cyber security risk assessment of a DDoS attack. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9866 s 183-202. doi:
  10.1007/978-3-319-45871-7_12
• Andersen, Lars Christian, Franke, Katrin & Shalaginov, Andrii (2016). Data-driven Approach to Information Sharing using Data Fusion and Machine Learning for Intrusion Detection. Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. 2016 s 19-30.
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• Banin, Sergii, Shalaginov, Andrii & Franke, Katrin (2016). Memory access patterns for malware detection. Norsk Informasjonssikkerhetskonferanse (NISK). ISSN 1893-6563. 2016 s 96-107.
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• Shalaginov, Andrii & Franke, Katrin (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. ISSN 1448-5869. 13(3-4) s 195-206. doi:
  10.3233/HIS-170236
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• Shalaginov, Andrii & Franke, Katrin (2015). A New Method for an Optimal SOM Size Determination in Neuro-Fuzzy for the Digital Forensics Applications. I Rojas, Ignacio, Joya, Gonzalo & Catala, Andreu (red.) Advances in Computational Intelligence; 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part II. Springer. ISBN 978-3-319-19222-2. s 549-563. doi:
  10.1007/978-3-319-19222-2_46
• Shalaginov, Andrii & Franke, Katrin (2015). A new method of fuzzy patches construction in Neuro-Fuzzy for malware detection. I Alonso, José M., Bustince, Humberto & Reformat, Marek (red.) Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology, Eusflat-15. Atlantis Press. ISBN 978-94-62520-77-6. s 170-177. doi:
  10.2991/ifsa-eusflat-15.2015.27
• Shalaginov, Andrii & Franke, Katrin (2015). Towards Improvement of Multinomial Classification Accuracy of Neuro-Fuzzy for Digital Forensics Applications. I Abraham, Ajith, Yong Han, Sang, Al-Sharhan, Salah A. & Liu, Hongbo (red.) Hybrid Intelligent Systems - proceedings of 15th International Conference HIS 2015 on Hybrid Intelligent Systems. Springer Publishing Company. ISBN 978-3-319-27220-7. s 199-210. doi:
  10.1007/978-3-319-27221-4_17
• Shalaginov, Andrii & Franke, Katrin (2015). Automated generation of fuzzy rules from large-scale network traffic analysis in Digital Forensics Investigations. I Köppen, Mario, Xue, Bing, Takagi, Hideyuki, Abraham, Ajith, Muda, Azah Kamilah & Ma, Kun (red.) 2015 Seventh International Conference of Soft Computing and Pattern Recognition (SoCPaR 2015). IEEE (Institute of Electrical and Electronics Engineers). ISBN 978-1-4673-9360-7. s 31-36. doi:
  10.1109/socpar.2015.7492778
• Shalaginov, Andrii & Franke, Katrin (2013). Automatic rule-mining for malware detection employing Neuro-Fuzzy Approach. I Rong, Chunming & Oleshchuk, Vladimir (red.) Proceeding of Norwegian Information Security Conference / Norsk informasjonssikkerhetskonferanse - NISK 2013 - Stavanger, 18th-20th November 2013. Akademika forlag. ISBN 978-82-321-0366-9. s 100-111. doi:
  10.2991/ifsa-eusflat-15.2015.27

Formidling

• Shalaginov, Andrii (2023). Artificial Intelligence for Cybersecurity in Resource-Constrained Smart Infrastructures: Applicability, Opportunities and Energy Modelling.
• Shalaginov, Andrii (2023). Panel Session on "The Impact of Artificial Intelligence on Enforcement Activities".
• Shalaginov, Andrii (2023). AI for Cybersecurity in Resource-Constrained Smart Infrastructures.
• Shalaginov, Andrii (2022). Cybersecurity in smart grid system.
• Shalaginov, Andrii (2022). The 6th International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2022.
• Shalaginov, Andrii (2022). Building AI-based middleware for cyber attacks detection in the next generation Smart Environments.
• Shalaginov, Andrii (2022). Artificial Intelligence for Cybersecurity in Resource-Constrained Smart Infrastructures: Applicability, Opportunities and Energy Modelling .
• Shalaginov, Andrii (2022). EU-prosjekt ENViSEC .
• Bhandari, Guru, Lyth, Andreas, Shalaginov, Andrii & Grønli, Tor-Morten (2022). Artificial Intelligence Enabled Middleware for Distributed Cyberattacks Detection in IoT-based Smart Environments.
  FULLTEKST
• Shalaginov, Andrii (2021). Digital Evidences Analysis of embedded devices and IoT Forensics .
• Shalaginov, Andrii & Grønli, Tor-Morten (2021). ENViSEC: Artificial Intelligence- enabled Cybersecurity for Future Smart Environments.
• Shalaginov, Andrii (2021). Artificial Intelligence and New Technologies.
• Shalaginov, Andrii (2021). The 5th International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2021.
• Shalaginov, Andrii & Grønli, Tor-Morten (2021). Workshop on Building Next Generation Internet: Artificial Intelligence-enabled Cybersecurity for IoT in Future Smart Environments.
• Shalaginov, Andrii (2021). ENViSEC project: Securing Smart Future.
• Shalaginov, Andrii (2021). AI-based cybersecurity research at Høyskolen Kristiania.
• Shalaginov, Andrii (2020). Identification and analysis of malware on selected suspected copyright-infringing websites .
• Shalaginov, Andrii (2019). Life after Ph.D: collaboration, networking and funding opportunities.
• Shalaginov, Andrii (2019). Digital Footprint and Cybersecurity.
• Shalaginov, Andrii, Franke, Katrin & Johnsen, Jan William (2018). The 2nd International Workshop on Big Data Analytic for Cyber Crime Investigation and Prevention 2018.
  FULLTEKST
• Shalaginov, Andrii (2017). Machine Learning Aided Malware Analysis - Research at NTNU.
• Shalaginov, Andrii (2017). Computational Forensics .
• Shalaginov, Andrii (2015). Automated generation of the human-understandable rules from network traffic dumps.
• Shalaginov, Andrii (2015). Application of Computational Intelligence for Digital Forensics.
• Shalaginov, Andrii & Franke, Katrin (2015). Generation of the human-understandable fuzzy rules from large-scale datasets for Digital Forensics applications using Neuro-Fuzzy.

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