Published August 15, 2022 | Version v1
Book Open

MODELING OF SECURITY SYSTEMS FOR CRITICAL INFRASTRUCTURE FACILITIES

Creators

  • 1. National Technical University "Kharkiv Polytechnic Institute"
  • 2. Sergei Korolyov Zhytomyr Military Institute
  • 3. Polissia National University
  • 4. Lviv Polytechnic National University
  • 5. Yaroslav Mudryi National Law University
  • 6. Hetman Petro Sahaidachnyi National Army Academy
  • 7. National Defence University of Ukraine named after Ivan Chernyakhovskyi
  • 8. Ternopil Ivan Puluj National Technical University

Description

The development of Industry 4.0 technologies is based on the rapid growth of the computing capabilities of mobile wireless technologies, which has made it possible to significantly expand the range of digital services and form a conglomeration of socio-cyber-physical systems and smart technologies. The First Section discusses the issues of building security systems based on the proposed Concept of multi-contour security systems, taking into account the hybridity and synergy of modern targeted cyber-attacks, their integration with social engineering methods. This approach not only increases the level of security, but also forms an objective approach to the use of post-quantum security mechanisms based on the proposed Lotka-Volterra models.

The Second Section analyzes the features of the functioning of social Internet services and establishes their role in ensuring the information security of the state. An approach is proposed to identify signs of threats in the text content of social Internet services, which will allow to quickly respond to changing situations and effectively counteract such threats. A classifier of information security profiles of users of social Internet services has been developed to assess the level of their danger as potential participants in disinformation campaigns. A method for identifying and evaluating the information and psychological impact on user communities in services is proposed. Models of conflict interaction of user groups in social Internet services are considered on the example of civil movements. To effectively counter threats to information security of the state, it is proposed to use the concept of synergistic user interaction and self-organization processes in a virtual community. Particular attention is paid to countering the manipulation of public opinion in the decision-making process by users of social Internet services.

The Third Section proposes a biometric security system that works to authenticate users based on a comparison of their fingerprints and certain templates stored in a biometric database. A method for determining the contour based on the passage of a curve and the filtering function of contour lines has been developed. The stage of skeletal identification is analyzed in detail. The Ateb-Gabor method with wave thinning has been developed. The performance of skeletal algorithms such as the Zhang-Suen thinning algorithm, the Hilditch algorithm, and the Ateb-Gabor method with wave decimation is analyzed. The presented results of experiments with biometric fingerprints based on the NIST Special Database 302 database showed the effectiveness of the proposed method. The software and firmware were developed using the Arduino Nano.

Files

110-Book Manuscript-419-2-10-20220816.pdf

Files (15.2 MB)

Name Size Download all
md5:ffecd558a646f8d14a5e151920335823
15.2 MB Preview Download

Additional details

References

  • Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2
  • Yevseiev, S., Melenti, Y., Voitko, O., Hrebeniuk, V., Korchenko, A., Mykus, S. et. al. (2021). Development of a concept for building a critical infrastructure facilities security system. Eastern-European Journal of Enterprise Technologies, 3 (9 (111)), 63–83. doi: http://doi.org/10.15587/1729-4061.2021.233533
  • Stoddart, K. (2016). UK cyber security and critical national infrastructure protection. International Affairs, 92 (5), 1079–1105. doi: http://doi.org/10.1111/1468-2346.12706
  • Konstantas, J. (2016). Dam Hackers! The Rising Risks to ICS and SCADA Environments. Security Week. Available at: http://www.securityweek.com/dam-hackers-rising-risks-ics-and-scada-environments
  • Westervelt, R. (2012). Old Application Vulnerabilities, Misconfigurations Continue to Haunt. TechTarget. Available at: http://searchsecurity.techtarget.com/feature/Old-Application-Vulnerabilities-Misconfigurations-Continue-to-Haunt
  • Ashford, W. (2014). Industrial control systems: What are the security challenges? Computer Weekly. Available at: http://www.computerweekly.com/news/2240232680/Industrial-control-systems-What-are-the-security-challenges
  • Shmatko, O., Balakireva, S., Vlasov, A., Zagorodna, N., Korol, O., Milov, O. et. al. (2020). Development of methodological foundations for designing a classifier of threats to cyberphysical systems. Eastern-European Journal of Enterprise Technologies, 3 (9 (105), 6–19. doi: https://doi.org/10.15587/1729-4061.2020.205702
  • Hryshchuk, R., Yevseiev, S., Shmatko, A. (2018). Construction methodology of information security system of banking information in automated banking systems. Vienna: Premier Publishing s. r. o.doi: http://doi.org/10.29013/r.hryshchuk_s.yevseiev_a.shmatko.cmissbiabs.284.2018
  • Kondratov S., Bobro D., Horbulin V. et. al.; Sukhodolia, O. (2017). Developing The Critical Infrastructure Protection System in Ukraine. Kyiv: NISS.
  • Rinaldi, S. M., Peerenboom, J. P., Kelly, T. K. (2001). Identifying, Understanding, and Analyzing Critical Infrastructure Dependencies. IEEE Control Systems Magazine, 21 (6), 11–25. doi: http://doi.org/10.1109/37.969131
  • Casalicchio, E., Galli, E., Tucci, S.; Setola, R.., Geretshuber, S. (Eds.) (2009). Modeling and Simulation of Complex Interdependent Systems: A Federated Agent-Based Approach. CRITIS 2008. LNCS. Heidelberg: Springer, 5508, 72–83. doi: http://doi.org/10.1007/978-3-642-03552-4_7
  • Haimes, Y. Y., Jiang, P. (2001). Leontief-Based Model of Risk in Complex Interconnected Infrastructures. Journal of Infrastructure Systems, 7 (1), 1–12. doi: http://doi.org/10.1061/(asce)1076-0342(2001)7:1(1)
  • Barker, K., Santos, J. R. (2010). Measuring the efficacy of inventory with a dynamic input–output model. International Journal of Production Economics, 126 (1), 130–143. doi: http://doi.org/10.1016/j.ijpe.2009.08.011
  • Santos, J. R. (2008). Interdependency analysis with multiple probabilistic sector inputs. Journal of Industrial & Management Optimization, 4 (3), 489–510. doi: http://doi.org/10.3934/jimo.2008.4.489
  • Jung, J. (2009). Probabilistic Extension to the Inoperability Input-Output Model: P-IIM. Charlottesville.
  • Santos, J. R., Haimes, Y. Y., Lian, C. (2007). A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies. Risk Analysis, 27 (5), 1283–1297. doi: http://doi.org/10.1111/j.1539-6924.2007.00957.x
  • Nieuwenhuijs, A., Luiijf, E., Klaver, M.; Papa, M., Shenoi, S. (Eds.) (2008). Modeling Dependencies Critical Infrastructures. Critical Infrastructure Protection II: Proceedings of the Second Annual IFIP Working Group 11.10 International Conference on Critical Infrastructure Protection. IFIP. Springer, Heidelberg, 290, 205–213. doi: http://doi.org/10.1007/978-0-387-88523-0
  • Rosato, V., Issacharoff, L., Tiriticco, F., Meloni, S., Porcellinis, S. D., Setola, R. (2008). Modelling interdependent infrastructures using interacting dynamical models. International Journal of Critical Infrastructures, 4 (1/2), 63–79. doi: http://doi.org/10.1504/ijcis.2008.016092
  • Panzieri, S., Setola, R. (2008). Failures propagation in critical interdependent infrastructures. International Journal of Modelling, Identification and Control, 3 (1), 69–78. doi: http://doi.org/10.1504/ijmic.2008.018186
  • Rinaldi, S. M. (2004). Modeling and Simulating Critical Infrastructures and Their Interdependencies. Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS 2004). IEEE Computer Society Press, Big Island. doi: http://doi.org/10.1109/hicss.2004.1265180
  • Forrester, J. W. (1961). Industrial Dynamics. Waltham: Pegasus Communications.
  • Forrester, J. W. (1961). Principles of Systems. Waltham: Pegasus Communications.
  • Gonzalez, J. J., Sarriegi, J. M., Gurrutxaga, A.; Lo´pez, J. (Ed.) (2006). A Framework for Conceptualizing Social En- gineering Attacks. CRITIS 2006. LNCS, vol. Heidelberg: Springer, 4347, 79–90. doi: http://doi.org/10.1007/11962977_7
  • Bier, V. M., Ferson, S., Haimes, Y. Y., Lambert, J. H., Small, M. J. (2004). Risk of Extreme and Rare Events: Lessons from a Selection of Approaches. Risk Analysis and Society: An Interdisciplinary Characterization of the Field. Cambridge: Cambridge University Press, 74–118. doi: http://doi.org/10.1017/cbo9780511814662.004
  • Bier, V. M. (2001). Game Theoretic Models for Critical Infrastructure Protection. Abstracts of the 2001 Society for Risk Analysis Annual Meeting "Risk Analysis in an Interconnected World".
  • von Neumann, J., Morgenstern, O. (1947). Theory of Games and Economic Behavior. Princeton: Princeton University Press.
  • Burke, D. A. (1999). Towards a Game Theory Model of Information Warfare. Air Force Institute of Technology, Wright-Patterson Air Force Base, 117.
  • Liu, D., Wang, X., Camp, J. (2008). Game-theoretic modeling and analysis of insider threats. International Journal of Critical Infrastructure Protection, 1, 75–80. doi: http://doi.org/10.1016/j.ijcip.2008.08.001
  • Jenelius, E., Westin, J., Holmgren, Å. J. (2010). Critical infrastructure protection under imperfect attacker perception. International Journal of Critical Infrastructure Protection, 3 (1), 16–26. doi: http://doi.org/10.1016/j.ijcip.2009.10.002
  • Yoshida, M., Kobayashi, K. (2010). Disclosure Strategies for Critical Infrastructure against Terror Attacks. Proceedings of the 2010 IEEE International Conference on Systems Man and Cybernetics (SMC 2010). Istanbul: IEEE Press, 3194–3199. doi: http://doi.org/10.1109/icsmc.2010.5642277
  • Major, J. A. (2002). Advanced Techniques for Modeling Terrorism Risk. The Journal of Risk Finance, 4 (1), 15–24. doi: http://doi.org/10.1108/eb022950
  • Lakdawalla, D. N., Zanjani, G. (2004). Insurance, Self-Protection, and the Economics of Terrorism. Tech. Rep. WR-171-ICJ, RAND Corporation. Santa Monica.
  • Woo, G. (2002). Quantitative Terrorism Risk Assessment. The Journal of Risk Finance, 4 (1), 7–14. doi: http://doi.org/10.1108/eb022949
  • Bier, V., Oliveros, S., Samuelson, L. (2007). Choosing What to Protect: Strategic Defensive Allocation against an Unknown Attacker. Journal of Public Economic Theory, 9 (4), 563–587. doi: http://doi.org/10.1111/j.1467-9779.2007.00320.x
  • Sandler, T., Siqueira, K. (2008). Games and Terrorism. Simulation & Gaming, 40 (2), 164–192. doi: http://doi.org/10.1177/1046878108314772
  • Bolloba´s, B. (1998). Modern Graph Theory. Graduate Texts in Mathematics, Vol. 184. Berlin: Springer.
  • Bolloba´s, B., Kozma, R., Miklo´s, D. (Eds.) (2009). Handbook of Large-Scale Random Networks. Bolyai Society Mathematical Studies, 18. Ja´nos Bolyai Mathematical Society and Springer. Budapest.
  • Albert, R., Baraba´si, A. L. (1999). Emergence of Scaling in Random Networks. Science, 286 (5439), 509–512. doi: http://doi.org/10.1126/science.286.5439.509
  • Albert, R., Baraba´si, A. L. (2002). Statistical Mechanics of Complex Networks. Reviews of Modern Physics 74 (1), 47–97. doi: http://doi.org/10.1103/revmodphys.74.47
  • Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45 (2), 167–256. doi: http://doi.org/10.1137/s003614450342480
  • Newman, M., Baraba´si, A. L., Watts, D. J. (Eds.) (2006). The Structure and Dynamics of Networks. Princeton Studies in Complexity. Princeton: Princeton University Press.
  • North, M.; Sallach, D., Wolsko, T. (Eds.) (2000). Agent-Based Modeling of Complex Infrastructures. In: Proceedings of the Workshop on Simulation of Social Agents: Architectures and Institutions. University of Chicago and Argonne National Laboratory, Chicago. ANL/DIS/TM-60, 239–250.
  • Zhu, G.-Y., Henson, M. A., Megan, L. (2001). Dynamic modeling and linear model predictive control of gas pipeline networks. Journal of Process Control, 11 (2), 129–148. doi: http://doi.org/10.1016/s0959-1524(00)00044-5
  • Han, Z. Y., Weng, W. G. (2010). An integrated quantitative risk analysis method for natural gas pipeline network. Journal of Loss Prevention in the Process Industries, 23 (3), 428–436. doi: http://doi.org/10.1016/j.jlp.2010.02.003
  • Wolthusen, S. D. (2005). GIS-based Command and Control Infrastructure for Critical Infrastructure Protection. Proceedings of the First IEEE International Workshop on Critical Infrastructure Protection (IWCIP 2005), 40–47. doi: http://doi.org/10.1109/iwcip.2005.12
  • Patterson, S. A., Apostolakis, G. E. (2007). Identification of Critical Locations Across Multiple Infrastructures for Terrorist Actions. Reliability Engineering & System Safety, 92 (9), 1183–1203. doi: http://doi.org/10.1016/j.ress.2006.08.004
  • Yevseiev, S., Pohasii, S., Milevskyi, S., Milov, O., Melenti, Y., Grod, I. et. al. (2021). Development of a method for assessing the security of cyber-physical systems based on the Lotka–Volterra model. Eastern-European Journal of Enterprise Technologies, 5 (9 (113)), 30–47. doi: http://doi.org/10.15587/1729-4061.2021.241638
  • Lippert, K. J., Cloutier, R. (2021). Cyberspace: A Digital Ecosystem. Systems, 9 (3), 48. doi: http://doi.org/10.3390/systems9030048
  • . Mazurczyk, W., Drobniak, S., Moore, S. Towards a Systematic View on Cybersecurity Ecology. Available at: https://arxiv.org/ftp/arxiv/papers/1505/1505.04207.pdf Last accessed: 25.06.2021
  • Gorman, S. P., Kulkarni, R. G., Schintler, L. A., Stough, R. R. (2004). A Predator Prey Approach to the Network Structure of Cyberspace. Available at: https://www.researchgate.net/publication/255679706_A_predator_prey_approach_to_the_network_structure_of_cyberspace Last accessed: 25.06.2021
  • Ya dogonyayu, ty ubegayesh'. Chto takoye model' Lotki-Vol'terry i kak ona pomogayet biologam. Available at: https://nplus1.ru/material/2019/12/04/lotka-volterra-model.
  • Tøndel, I. A., Cruzes, D. S., Jaatun, M. G., Sindre, G. (2022). Influencing the security prioritisation of an agile software development project. Computers & Security, 118, 102744. doi: http://doi.org/10.1016/j.cose.2022.102744
  • Șcheau, M.-C., Leu, M.-D., Udroiu, C. (2022). At the Intersection of Interests and Objectives in Cybersecurity. Proceedings of the International Conference on Cybersecurity and Cybercrime (IC3), 29–34. doi: http://doi.org/10.19107/cybercon.2022.03
  • Mohammed, N. Q., Amir, A., Salih, M. H., Ahmad, B. (2022). Design and Implementation of True Parallelism Quad-Engine Cybersecurity Architecture on FPGA. International Journal of Advanced Computer Science and Applications, 13 (1), 719–724. doi: http://doi.org/10.14569/ijacsa.2022.0130183
  • Nevliudov, I., Yevsieiev, V., Maksymova, S., Filippenko, I. (2020). Development of an architectural-logical model to automate the management of the process of creating complex cyber-physical industrial systems. Eastern-European Journal of Enterprise Technologies, 4 (3 (106)), 44–52. doi: http://doi.org/10.15587/1729-4061.2020.210761
  • Szymanski, T. H. (2022). The "Cyber Security via Determinism" Paradigm for a Quantum Safe Zero Trust Deterministic Internet of Things (IoT). IEEE Access, 10, 45893–45930. doi: http://doi.org/10.1109/access.2022.3169137
  • de Kinderen, S., Kaczmarek-Heß, M., Hacks, S. (2022). Towards Cybersecurity by Design: A multi-level reference model for requirements-driven smart grid cybersecurity. ECIS 2022 Research Papers. 89. Available at: https://aisel.aisnet.org/ecis2022_rp/89/
  • Do Thu, H., Hoang, N. X., Hoang N. V., Du, N. H., Huong, T. T., Phuc Tran, K. (2022). Explainable Anomaly Detection for Industrial Control System Cybersecurity. Available at:: https://www.researchgate.net/publication/360383589_Explainable_Anomaly_Detection_for_Industrial_Control_System_Cybersecurity
  • KNX Technical Manual 2CKA001473B8668. (2017). KNX Technical Manual. Busch-Presence detector KNX / Busch-Watchdog Sky KNX. Busch-Jaeger Elektro GmbH, 198.
  • ABB i-bus KNX KNX Security Panel GM/A 8.1 Product Manual (2016). Busch-Watchdog Sky KNX. Busch-Jaeger Elektro GmbH, 648.
  • Pohasii, S., Yevseiev, S., Zhuchenko, O., Milov, O., Lysechko, V., Kovalenko, O. et. al. (2022). Development of crypto-code constructs based on LDPC codes. Eastern-European Journal of Enterprise Technologies, 2 (9 (116)), 44–59. doi: http://doi.org/10.15587/1729-4061.2022.254545
  • Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489 (7415), 295–298. doi: http://doi.org/10.1038/nature11421
  • Parthasarathy, S., Ruan, Y., Satuluri, V. (2011). Community Discovery in Social Networks: Applications, Methods and Emerging Trends. Social Network Data Analytics, 79–113. doi: http://doi.org/10.1007/978-1-4419-8462-3_4
  • Madirolas, G., de Polavieja, G. G. (2015). Improving Collective Estimations Using Resistance to Social Influence. PLOS Computational Biology, 11 (11), e1004594. doi: http://doi.org/10.1371/journal.pcbi.1004594
  • Sun, J., Tang, J. (2011). A survey of models and algorithms for social influence analysis Social Network Data Analytics, 177–214. doi: http://doi.org/10.1007/978-1-4419-8462-3_7
  • Anagnostopoulos, A., Kumar, R., Mahdian, M. (2008). Influence and correlation in social networks. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'08), 7–15. doi: http://doi.org/10.1145/1401890.1401897
  • Goyal, A., Bonchi, F., Lakshmanan, L. V. (2010). Learning influence probabilities in social networks. In Proceedings of the 3st ACM International Conference on Web Search and Data Mining (WSDM'10), 207–217. doi: http://doi.org/10.1145/1718487.1718518
  • Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. In Proceeding of the 19th international conference on World Wide Web (WWW'10), 981–990. doi: http://doi.org/10.1145/1772690.1772790
  • Scripps, J., Tan, P.-N., Esfahanian, A.-H. (2009). Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'09), 747–756. doi: http://doi.org/10.1145/1557019.1557102
  • Tang, L., Liu, H. (2009). Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'09), 817–826. doi: http://doi.org/10.1145/1557019.1557109
  • Horovyi, V. M., Onyshchenko, O. S., Polovynchak, Yu. M., Horova, S. V., Kostenko, L. Y., Matviichuk, A. V. et. al. (2015). Tekhnolohii rozvytku i zakhystu natsionalnoho informatsiinoho prostoru. Kyiv: NBUV, 296.
  • Konakh, V. K. (2014). Natsionalnyi informatsiinyi prostir Ukrainy: problemy formuvannia ta derzhavnoho rehuliuvannia. Kyiv: NISD.
  • Onyshchenko, O. S., Horovyi, V. M., Popyk, V. I. (2014). Sotsialni merezhi yak instrument vzaiemovplyvu vlady ta hromadianskoho suspilstva. Kyiv: NAN Ukrainy, Nats. b-ka Ukrainy im. V. I. Vernadskoho, 260.
  • Danik, Yu., Hryshchuk, R., Samchyshyn, O. (2015). Mobile social Internet services as the modern mass communication. Ukrainian Scientific Journal of Information Security, 21 (1), 16–20.
  • Peleshchyshyn, A. M., Sierov, Yu. O., Berezko, O. L., Peleshchyshyn. O. P., Tymovchak-Maksymets, O. Yu., Markovets, O. V. (2012). Protsesy upravlinnia interaktyvnymy sotsialnymy komunikatsiiamy v umovakh rozvytku informatsiinoho suspilstva. Lviv, Vyd-vo Lviv. Politekhniky, 368.
  • Hryshchuk, R. V., Danyk, Yu. H. (2016). Osnovy kibernetychnoi bezpeky. Zhytomyr: ZhNAEU, 636.
  • Hryshchuk, R. V., Molodetska-Hrynchuk, K. V. (2017). Postanovka problemy zabezpechennia informatsiinoi bezpeky derzhavy u sotsialnykh internet-servisakh. Suchasnyi zakhyst informatsii, 3 (31), 86–96.
  • Tufekci, Z., Wilson, C. (2012). Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square. Journal of Communication, 62 (2), 363–379. doi: http://doi.org/10.1111/j.1460-2466.2012.01629.x
  • Savanevskyi, M. (2013). #ievromaidan: ukrainska tsyfrova revoliutsiia ta ostannii shans analohovym politykam staty tsyfrovymy. Watcher. Available at: http://watcher.com.ua/2013/11/29/yevromaydan-ukrayinska-tsyfrova-revolyutsiya-ta-ostanniy-shans-analohovym-politykam-staty-tsyfrovymy/
  • Barovska, A. (2016). Informatsiini vyklyky hibrydnoi viiny: kontent, kanaly, mekhanizmy protydii. Kyiv: NISD, 109.
  • Holloway, M. (2017). How Russia weaponized social media in the Crimea. Realcleardefense.com. Available at: https://www.realcleardefense.com/articles/2017/05/10/how_russia_weaponized_social_media_in_crimea_111352.html
  • Perry, B. (2015). Non-linear warfare in Ukraine: the critical role of information operations and special operations. Small Wars Journal, 11 (1). Available at: http://smallwarsjournal.com/jrnl/art/non-linear-warfare-in-ukraine-the-critical-role-of-information-operations-and-special-opera
  • Obzor sotsyalnykh setei. Leto, 2016 (2016). Slideshare. Available at: https://www.slideshare.net/adproisobar/2016-64479518
  • Pro zastosuvannia personalnykh spetsialnykh ekonomichnykh ta inshykh obmezhuvalnykh zakhodiv (sanktsii) (2017). Ukaz Prezydenta Ukrainy No. 133/2017. Available at: http://www.president.gov.ua/documents/1332017-21850
  • Hryshchuk, R., Molodetska-Hrynhchuk, K. (2018). Methodological Foundation of State's Information Security in Social Networking Services in Conditions of Hybrid War. Information & Security: An International Journal, 41, 61–79. doi: http://doi.org/10.11610/isij.4105
  • Molodetska-Hrynchuk, K. (2017). Outreaches content tracing technique for social networking services. Radio electronics, Computer Science, Control, 2 (41), 117–126. doi: http://doi.org/10.15588/1607-3274-2017-2-13
  • Manning, Chr., Raghavan, P., Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. doi: http://doi.org/10.1017/cbo9780511809071
  • Wardle, C., Derakhshan, H. (2017). Information disorder: toward an interdisciplinary framework for research and policy making. Available at: https://rm.coe.int/information-disorder-toward-an-interdisciplinary-framework-for-researc/168076277c
  • Barabash, O. V., Hryshchuk, R. V., Molodetska-Hrynchuk, K. V. (2018). Identification threats to the state information security in the text content of social networking services. Science-Based Technologies, 38 (2), 232–239. doi: http://doi.org/10.18372/2310-5461.38.12855
  • Pennacchiotti, M., Popescu, A.-M. (2011). Democrats, republicans and starbucks afficionados. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '11. doi: http://doi.org/10.1145/2020408.2020477
  • Beller, C., Knowles, R., Harman, C., Bergsma, S., Mitchell, M., Van Durme, B. (2014). I'm a Belieber: social roles via self-identification and conceptual attributes. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 181–186. doi: http://doi.org/10.3115/v1/p14-2030
  • Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M. et. al. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLoS ONE, 8 (9), e73791. doi: http://doi.org/10.1371/journal.pone.0073791
  • Gore, R. J., Diallo, S., Padilla, J. (2015). You Are What You Tweet: Connecting the Geographic Variation in America's Obesity Rate to Twitter Content. PLOS ONE, 10 (9), e0133505. doi: http://doi.org/10.1371/journal.pone.0133505
  • Molodetska, K., Tymonin, Y. (2019). System-dynamic models of destructive informational influence in social networking services. International Journal of 3D Printing Technologies and Digital Industry, 3 (2), 137–146.
  • Pasca, M. (2007). What you seek is what you get: Extraction of class attributes from query logs. Proceedings of IJCAI.
  • Faraz, A. (2016). A Comparison of Text Categorization Methods. International Journal on Natural Language Computing, 5 (1), 31–44. doi: http://doi.org/10.5121/ijnlc.2016.5103
  • Fernández-Martínez, F., Zablotskaya, K., Minker, W. (2012). Text categorization methods for automatic estimation of verbal intelligence. Expert Systems with Applications, 39 (10), 9807–9820. doi: http://doi.org/10.1016/j.eswa.2012.02.173
  • Natekin, A., Knoll, A.(2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7 (21), 1–21. doi: http://doi.org/10.3389/fnbot.2013.00021
  • Freund, Y., Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55 (1), 119–139. doi: http://doi.org/10.1006/jcss.1997.1504
  • "MIB Datasets" (2017). Mib.projects.iit.cnr.it. Available at: http://mib.projects.iit.cnr.it/dataset.html
  • Hryshchuk, R. V., Mamariev, V. M., Molodetska-Hrynchuk K. V. (2017). Klasyfikatsiia profiliv informatsiinoi bezpeky aktoriv v sotsialnykh internet-servisakh (na prykladi mikroblohu Twitter). Informatsiini tekhnolohii ta kompiuterna inzheneriia, 2, 12–19.
  • Cresci, S., Pietro, R. Di, Petrocchi, M., Spognardi, A., Tesconi, M. (2015). Fame for sale: efficient detection of fake Twitter followers. Decision Support Systems, 80, 56–71. doi: http://doi.org/10.48550/arXiv.1509.04098
  • Milan, S. (2015). From social movements to cloud protesting: the evolution of collective identity. Information, Communication & Society, 18 (8), 887–900. doi: http://doi.org/10.1080/1369118x.2015.1043135
  • Panchenko, V. M. (2009). Linhvostatystychni oznaky manipuliuvannia suspilnoiu svidomistiu v zasobakh masovoi komunikatsii. Suchasni informatsiini tekhnolohii u sferi bezpeky ta oborony, 1 (4), 81–85.
  • Molodetska, K., Brodskiy, Yu., Fedushko, S. (2020). Model of assessment of information-psychological influence in social networking services based on information insurance. Control, Optimisation and Analytical Processing of Social Networks : Proc. of the 2nd International Workshop on COAPSN-2020, 2616, 187–198. Available at: http://ceur-ws.org/Vol-2616/paper16.pdf
  • Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A. et. al. (2018). Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate. American Journal of Public Health, 108 (10), 1378–1384. doi: http://doi.org/10.2105/ajph.2018.304567
  • The official website of the World Health Organization. Available at: https://www.who.int/home
  • Molodetska, K., Tymonin, Y., Melnychuk, I. (2020). The conceptual model of information confrontation of virtual communities in social networking services. International Journal of Electrical and Computer Engineering (IJECE), 10 (1), 1043–1052. doi: http://doi.org/10.11591/ijece.v10i1.pp1043-1052
  • Leask, J., Kinnersley, P., Jackson, C., Cheater, F., Bedford, H., & Rowles, G. (2012). Communicating with parents about vaccination: a framework for health professionals. BMC Pediatrics, 12 (1). doi: http://doi.org/10.1186/1471-2431-12-154
  • Hryshchuk, R., Molodetska, K., Tymonin, Yu. (2019). Modelling of conflict interaction of virtual communities in social networking services on an example of anti-vaccination movement. Conflict Management in Global Information Networks : Proc. of the International Workshop on CMiGIN-2019, 2588, 250–264.
  • Kolesnykov, A. A. (2005). Synerhetycheskoe metody upravlenyia slozhnymy systemamy: teoryia systemnoho synteza. Edytoral URSS.
  • Hryshchuk, R., Molodetska, K. (2017). Synergetic control of social networking services actors' interactions. Recent Advances in Systems, Control and Information Technology. Springer International Publishing, 543, 34–42. doi: http://doi.org/10.1007/978-3-319-48923-0_5
  • Hryshchuk, R., Molodetska, K., Serov, Y. (2019). Method of improving the information security of virtual communities in social networking services. Proc. of the 1st International Workshop on Control, Optimisation and Analytical Processing of Social Networks, 2392, 23-41.
  • Wu, B., Cheng, T., Yip, T. L., Wang, Y. (2020). Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes. Ocean Engineering, 197, 106909. doi: http://doi.org/10.1016/j.oceaneng.2019.106909
  • Molodetska, K., Solonnikov, V., Voitko, O., Humeniuk, I., Matsko, O., Samchyshyn, O. (2021). Counteraction to information influence in social networking services by means of fuzzy logic system. International Journal of Electrical and Computer Engineering, 11 (3), 2490–2499. doi: http://doi.org/10.11591/ijece.v11i3.pp2490-2499
  • Akgun, A., Sezer, E. A., Nefeslioglu, H. A., Gokceoglu, C., Pradhan, B. (2012). An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers & Geosciences, 38 (1), 23–34. doi: http://doi.org/10.1016/j.cageo.2011.04.012
  • Molodetska, K., Tymonin, Y., Markovets, O., Melnychyn, A. (2020). Phenomenological model of information operation in social networking services. Indonesian Journal of Electrical Engineering and Computer Science, 19 (2), 1078. doi: https//doi.org/10.11591/ijeecs.v19.i2.pp1078-1087
  • Synko, A., Molodetska, K. (2021). Application of Clusterization for Analysis of Virtual Community Users. Information Technologies & Applied Sciences: Proc. of the Symposium on IT&AS 2021, 2824, 9–19.
  • How the Social Networks Affect Politics in Ukraine: Conclusions of the Research (2020). Internews.Ua. Avaliable at: https://internews.ua/opportunity/social-network-research
  • Criminal complaint. Official website of the U.S. Department of Justice (DOJ). Available at: https://www.justice.gov/opa/press-release/file/1102316/download
  • Peleshchyshyn, O., Molodetska, K., Solianyk, A., Kravets, R. (2019). Modelling the complex of automation of company marketing activity in online communities. Conflict Management in Global Information Networks: Proc. of the International Workshop on CMiGIN 2019, 2588, 301–310.
  • Maddux, R. D. (2014). Arrow's theorem for incomplete relations. Journal Of Logical And Algebraic Methods In Programming, 83 (2), 235–248. doi: http://doi.org/10.1016/j.jlap.2014.02.012
  • Cato, S. (2018). Incomplete decision-making and Arrow's impossibility theorem. Mathematical Social Sciences, 94, 58–64. doi: http://doi.org/10.1016/j.mathsocsci.2017.10.002
  • Brindley, P., Cameron, R. W., Ersoy, E., Jorgensen, A., Maheswaran, R. (2019). Is more always better? Exploring field survey and social media indicators of quality of urban greenspace, in relation to health. Urban Forestry & Urban Greening, 39, 45–54. doi: http://doi.org/10.1016/j.ufug.2019.01.015
  • Public opinion poll (2020). Ilko Kucheriv Democratic Initiatives Foundation. Kyiv.
  • Karpov, А. V. (2009). Theorem on the impossibility of proportional representation. HSE Economic Journal, 4, 596–615.
  • Arrow, K. J. (1950). A Difficulty in the Concept of Social Welfare. Journal of Political Economy, 58 (4), 328–346. doi: http://doi.org/10.1086/256963
  • Vasiljev, S. (2008). Manipulability of a voting. SSRN Electronic Journal, 2008. doi: http://doi.org/10.2139/ssrn.1118627
  • Molodetska, K. (2020). Counteraction to strategic manipulations on actors' decision making in social networking services. 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), 266–269. doi: http://doi.org/10.1109/atit50783.2020.9349347
  • Khromov, D. V. (2013). Models and algorithms for constructing curvilinear skeletons of spatial forms. Moscow: MV Lomonosov Moscow State University, 23.
  • Hrytsyk, V., Nazarkevych, M. (2021). Research on the Increase of Information Theory in the Era of the Ending of Silicon Electronics and New Types of Risks. CEUR Workshop Proceedingsthis, 3101, 65–82.
  • Li, J., Glover, J. D., Zhang, H., Peng, M., Tan, J., Mallick, C. B. et. al. (2022). Limb development genes underlie variation in human fingerprint patterns. Cell, 185 (1), 95–112. doi: http://doi.org/10.1016/j.cell.2021.12.008
  • Hrytsyk, V., Grondzal, A., Bilenkyj, A. (2015). Augmented reality for people with disabilities. Computer Sciences and Information Technologies, 188–191. doi: http://doi.org/10.1109/stc-csit.2015.7325462
  • Prasad, M. V. D., Krishna, N. S., Ahammad, S. H., Kumar, G. N. S. (2020). Security Systems For Identification And Detection Fingerprint Based On Cnn And Fcn. International Journal of Scientific & Technology Research 9 (2), 1668–1672.
  • Bontrager, P., Togelius, J., Memon, N. (2017). Deepmasterprint: Generating fingerprints for presentation attacks. arXiv preprint arXiv: 1705.07386.
  • Bontrager, P., Roy, A., Togelius, J., Memon, N., & Ross, A. (2018, October). Deepmasterprints: Generating masterprints for dictionary attacks via latent variable evolution. 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 1–9. doi: http://doi.org/10.1109/btas.2018.8698539
  • Teslyuk, V. M., Beregovskyi, V. V., Pukach, A. I. (2013). Development of smart house system model based on colored Petri nets. 2013 XVIIIth International Seminar / Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), 205-208.
  • Drets, G., Liljenström, H. (1998). Fingerprint Sub-Classification and Singular Point Detection. International Journal of Pattern Recognition and Artificial Intelligence, 12 (4), 407–422. doi: http://doi.org/10.1142/s0218001498000269
  • Ali, S. M., Al-Zewary, M. S. (1997). A new fast automatic technique for fingerprints recognition and identification. Journal of the Islamic Academy of Sciences, 10 (2), 55–60.
  • Cao, J., Dai, 'Q. (2009). A novel online fingerprint segmentation method based on frame-difference,' 2009 International Conference on Image Analysis and Signal Processing, Taizhou, 57–60. doi: http://doi.org/10.1109/iasp.2009.5054651
  • Zhan, X., Sun, Z., Yin, Y., Chen, Y. (2005). Fingerprint image segmentation method based on MCMC & GA. In International Conference on Image Analysis and Processing. Berlin, Heidelberg: Springer, 391-398. doi: http://doi.org/10.1007/11553595_48
  • Tsmots, I., Skorokhoda, O., Rabyk, V. Structure Software Model of a Parallel-Vertical Multi-input Adder for FPGA Implementation. Computer Sciences and Information Technologies – Proceedings of the 11th International Scientific and Technical Conference CSIT 2016. Lviv, 158–160. doi: http://doi.org/10.1109/stc-csit.2016.7589894
  • Tsmots, I., Skorokhoda, O. (2010). Methods and VLSI-structures for Neural Element Implementation. Perspective Technologies and Methods in MEMS Design, MEMSTECH'2010 – Processing of the 6th International Conference. Polyana, 135.
  • Ding, Y., Zhuang, D., Wang, K. (2005). A study of hand vein recognition method. IEEE International Conference Mechatronics and Automation, 4, 2106–2110. doi: http://doi.org/10.1109/icma.2005.1626888
  • Rathgeb, C., Uhl, A. (2010). Two-factor authentication or how to potentially counterfeit experimental results in biometric systems. In International Conference Image Analysis and Recognition. Berlin, Heidelberg: Springer, 296–305. doi: http://doi.org/10.1007/978-3-642-13775-4_30
  • Denysyuk, P., Teslyuk, V., Chorna, I. (2018). Development of mobile robot using LIDAR technology based on Arduino controller. 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), 240–244. doi: http://doi.org/10.1109/memstech.2018.8365742
  • Kunanets, N., Vasiuta, O., Boiko, N. (2019). Advanced technologies of big data research in distributed information systems. 2019 IEEE 14th International Conference on Computer Science and Information Technologies (CSIT), 3, 71–76. doi: http://doi.org/10.1109/stc-csit.2019.8929756
  • Hore, A., Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM. 2010 20th International Conference on Pattern Recognition. IEEE, 2366–2369. doi: http://doi.org/10.1109/icpr.2010.579
  • "Dronyuk, I., Nazarkevych, M., & Fedevych, O. (2015, October). Synthesis of Noise-Like Signal Based on Ateb-Functions. In International Conference on Distributed Computer and Communication Networks (pp. 132-140). Springer, Cham.
  • Putyatin, E. P., Panchenko, I. A. (2010). Invariance of features in problems of image processing with a pronounced texture. Radioelectronics and Informatics, 1. Available at: https://cyberleninka.ru/article/n/invariantnost-priznakov-v-zadachah-obrabotki-izobrazheniy-s-yarkovyrazhennoy-teksturoy Last accessed: 31.05.2022
  • Boyko, N., Shakhovska, N. (2018). Prospects for using cloud data warehouses in information systems. 2018 IEEE 13th International Scientific and Technical Conference on Computer Science and Information Technologies (CSIT), 2, 136–139. doi: http://doi.org/10.1109/stc-csit.2018.8526745
  • Nazarkevych, M., Voznyi, Y., Hrytsyk, V., Klyujnyk, I., Havrysh, B., Lotoshynska, N. (2021). Identification of biometric images by machine learning. 2021 IEEE 12th International Conference on Electronics and Information Technologies. ELIT 2021 – Proceedings, 95–98. doi: http://doi.org/10.1109/elit53502.2021.9501064
  • Nazarkevych, M., Kynash, Y., Oliarnyk, R., Klyujnyk, I., Nazarkevych, H. (2017). Application perfected wave tracing algorithm. 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 1011–1014. doi: http://doi.org/10.1109/ukrcon.2017.8100403
  • Lyubchenko, V. A. (2002). Application of one-dimensional normalization in image recognition problems with projective distortions. Radio Electronics and Youth in the XXI Century. Kharkiv: KhNURE, 388–389.
  • Nazarkevych, M., Oliiarnyk, R., Nazarkevych, H., Kramarenko, O., Onyshschenko, I. (2016). The method of encryption based on Ateb-functions. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), 129–133. doi: http://doi.org/10.1109/dsmp.2016.7583523
  • Medykovskyy, M., Lipinski, P., Troyan, O., Nazarkevych, M. (2015). Methods of protection document formed from latent element located by fractals. Computer Sciences and Information Technologies (CSIT). IEEE, 70–72. doi: http://doi.org/10.1109/stc-csit.2015.7325434
  • Süsstrunk, S., Buckley, R., Swen, S. (1999, January). Standard RGB color spaces. In Color and imaging conference (Vol. 1999, No. 1, pp. 127-134). Society for Imaging Science and Technology.
  • Dronyuk, I., Nazarkevych, M, Poplavska, Z. (2017). Gabor generalization filters based on ateb-functions for information security. InInternational Conference on Man – Machine Interactions 2017. Cham: Springer, 195–206. doi: http://doi.org/10.1007/978-3-319-67792-7_20
  • He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778. doi: http://doi.org/10.1109/cvpr.2016.90
  • Wang, L., Yuan, W., Zeng, L., Xu, J., Mo, Y., Zhao, X., Peng, L. (2022). Dementia analysis from functional connectivity network with graph neural networks. Information Processing & Management, 59 (3), 102901. doi: http://doi.org/10.1016/j.ipm.2022.102901
  • Chen, Y., Sanghavi, S., Xu, H. (2012). Clustering sparse graphs. Advances in neural information processing systems, 25.
  • Arulselvan, A., Commander, C. W., Elefteriadou, L., Pardalos, P. M. (2009). Detecting critical nodes in sparse graphs. Computers & Operations Research, 36 (7), 2193–2200. doi: http://doi.org/10.1016/j.cor.2008.08.016
  • Ashraf, I., Hur, S., Park, S., Park, Y. (2020). DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier. Sensors, 20 (1), 133. doi: http://doi.org/10.3390/s20010133
  • Chen, W., Sui, L., Xu, Z., Lang, Y. (2012). Improved Zhang-Suen thinning algorithm in binary line drawing applications. 2012 International Conference on Systems and Informatics (ICSAI2012). IEEE, 1947–1950. doi: http://doi.org/10.1109/icsai.2012.6223430
  • Saoji, S. U., Dua, N., Choudhary, A. K., Phogat, B. (2021). Air Canvas Application Using OpenCV and Numpy in Python. IRJET, 8 (8).
  • Nazarkevych, M., Oliarnyk, R., Troyan, O., Nazarkevych, H. (2016). Data protection based on encryption using Ateb-functions. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). IEEE, 30–32.
  • Nazarkevych, M., Dmytruk, S., Hrytsyk, V., Vozna, O., Kuza, A., Shevchuk, O. et. al. (2021). Evaluation of the Effectiveness of Different Image Skeletonization Methods in Biometric Security Systems. International Journal of Sensors, Wireless Communications and Control, 11 (5), 542–552. doi: http://doi.org/10.2174/2210327910666201210151809
  • Artemenko, M. V., Kalugina, N. M., Shutkin, A. N. (2016). Formation of a set of informative indicators on the basis of the Kolmogorov-Gabor approximating polynomial and the maximum gradient of functional differences. Proceedings of Southwestern State University. Series: Management, computer engineering, computer science. Medical Instrumentation, 1, 116–123.
  • Nazarkevych, M., Buriachok, V., Lotoshynska, N., Dmytryk, S. (2018). Research of Ateb-Gabor filter in biometric protection systems. 2018 IEEE 13th International Scientific and Technical Conference on Computer Science and Information Technologies (CSIT), 1, 310–313. doi: http://doi.org/10.1109/stc-csit.2018.8526607
  • Nazarkevych, M., Logoyda, M., Troyan, O., Vozniy, Y., Shpak, Z. (2019). The ateb-gabor filter for fingerprinting. Conference on Computer Science and Informatics, 247–255. doi: http://doi.org/10.1007/978-3-030-33695-0_18
  • Nazarkevych, M., Oliarnyk, R., Dmytruk, S. (2017). An images filtration using the Ateb-Gabor method. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), 1, 208–211. doi: http://doi.org/10.1109/stc-csit.2017.8098770
  • Nazarkevych, M., Riznyk, O., Samotyy, V., Dzelendzyak, U. (2019). Detection of regularities in the parameters of the ateb-gabor method for biometric image filtration. Eastern European Journal of Advanced Technology, 1 (2 (97)), 57–65. doi: http://doi.org/10.15587/1729-4061.2019.154862
  • Medykovskyi, M. O., Tsmots, I. G.,Tsymbal, Y. V. (2016). Information analytical system for energy efficiency management at enterprises in the city of Lviv (Ukraine). Actual Problems in Economics, 1 (175), 379–384.
  • Dronjuk, I., Nazarkevych, M., Troyan, O. (2016). The modified amplitude-modulated screening technology for the high printing quality. InInternational Symposium on Computer and Information Sciences. Cham: Springer, 270–276. doi: http://doi.org/10.1007/978-3-319-47217-1_29
  • Nazarkevych, M., Lotoshynska, N., Brytkovskyi, V., Dmytruk, S., Dordiak, V., Pikh, I. (2019). Biometric Identification System with Ateb-Gabor Filtering. 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT), 15–18. doi: http://doi.org/10.1109/elit.2019.8892282
  • Nazarkevych, M., Lotoshynska, N., Hrytsyk, V., Havrysh, B., Vozna, O., Palamarchuk, O. (2021). Design of biometric system and modeling of machine learning for entering the information system. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2, 225–230. doi: http://doi.org/10.1109/csit52700.2021.9648770