The depiction of cybercrime victims using data mining techniques
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- Category: Content №5 2022
- Last Updated on 30 October 2022
- Published on 30 November -0001
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Authors:
T.A.Vasilyeva, orcid.org/0000-0003-0635-7978, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; Silesian University of Technology, Gliwice, the Republic of Poland; The London Academy of Science and Business, London, the United Kingdom of Great Britain and Northern Ireland
O.V.Kuzmenko, orcid.org/0000-0001-8520-2266, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
N.V.Stoyanets, orcid.org/0000-0002-7526-6570, Sumy National Agrarian University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
A.E.Artyukhov, orcid.org/0000-0003-1112-6891, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V.V.Bozhenko, orcid.org/0000-0002-9435-0065, Sumy State University, Sumy, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; Tubingen University, Tubingen, the Federal Republic of Germany
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2022, (5): 174 - 178
https://doi.org/10.33271/nvngu/2022-5/174
Abstract:
Purpose. Development of a scientific and methodological approach for creating a phase depiction of a cybercrime victim by identifying significant personified characteristics.
Methodology. In the process of research, methods of systematization, comparison, grouping, logical generalization, bibliometric analysis, regression analysis (the method of sigma-limited parameterization), and the algorithm of associative rules were used.
Findings. According to the results of the research, it was established that the countries with the highest rates of cyber fraud in the field of financial services include Luxembourg (15%), France (14%), Great Britain (13%) and Denmark (11%) are among. In 2020, on average, every 10th resident of the European Union became a cyber victim when performing financial transactions. The results of an empirical analysis using the algorithm of associative rules showed that in 100% of the analyzed cases of cyber fraud in the field of financial services among European residents, stable patterns are found between the following parameters: a married woman who raises three children, a woman aged 5564 years old who raises three children, a married person who periodically experiences financial difficulties and raises three children, a person who lives in a rural area and raises three children, a person aged 6574, who has three children. In addition, the probability for a woman raising three children of becoming a victim of cyber fraud is 87.5%. In 71.4% of cybercrime cases in the field of financial services, a close causal relationship is traced with the following parameters: manual worker who was cyberattacked through a smartphone, a person who periodically experiences financial difficulties and a cyberattack occurred through a smartphone.
Originality. The use of profiling technology allows evaluating and predicting the behavior of the financial services consumer in the conditions of the growing risk of cyber fraud based on the systematization and establishment of cause-and-effect relationships between the most informative personalized signs of them.
Practical value. The development of a phase depiction of a probable victim of cybercrime in the financial system allows identifying the signs of a cyber threat in the early stages, and immediately reacting to it, thus neutralizing or minimizing the negative consequences. The results of the conducted research will have practical significance for the management of financial institutions and public organizations that specialize in training and raising the level of financial and digital literacy of citizens.
Keywords:victim, cybercrime, associative rules, financial institutions
References:
1. Caught in the Crosshairs: Are Utilities Keeping Up with the Industrial Cyber Threat? (n.d.). Retrieved from https://assets.siemens-energy.com/siemens/assets/api/uuid:c723efb9-847f-4a33-9afa-8a097d81ae19/siemens-cybersecurity.pdf.
2. Lopez, B.S., & Alcaide, A.V. (2020). Blockchain, AI and IoT to Improve Governance, Financial Management and Control of Crisis: Case Study COVID-19.SocioEconomic Challenges, 4(2), 78-89. https://doi.org/10.21272/sec.4(2).78-89.2020.
3. Obeid, H., Hillani, F., Fakih, R., &Mozannar, K.(2020). Artificial Intelligence: Serving American Security and Chinese Ambitions. Financial Markets, Institutions and Risks, 4(3), 42-52. https://doi.org/10.21272/fmir.4(3).42-52.2020.
4. Al-Tahat, S., & Moneim, O.A. (2020). The impact of artificial intelligence on the correct application of cyber governance in Jordanian commercial banks. International Journal of Scientific and Technology Research, 9(3).
5. Berdyugin, A.A., & Revenkov, P.V. (2020). Cyberattack risk assessment in electronic banking technologies (the case of software implementation). Finance: Theory and Practice, 24(6). https://doi.org/10.26794/2587-5671-2020-24-6-51-60.
6. Yarovenko, H., Bilan, Y., Lyeonov, S., & Mentel, G. (2021). Methodology for assessing the risk associated with information and knowledge loss management. Journal of Business Economics and Management, 22(2), 369-387. https://doi.org/10.3846/jbem.2021.13925.
7. Leonov, S., Yarovenko, H., Boiko, A., & Dotsenko, T. (2019). Information system for monitoring banking transactions related to money laundering. CEUR Workshop Proceedings, 2422, 297-307. Retrieved from http://ceur-ws.org/.
8. Noor, U., Anwar, Z., Amjad, T., & Choo, K.K.R. (2019). A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise. Future Generation Computer Systems, 96. https://doi.org/10.1016/j.future.2019.02.013.
9. Kuznetsov, A., Datsenko, S., Gorbenko, Y., Chupilko, T., Korneyev, M., & Klym, V. (2021). Experimental researches of biometric authentication using convolutional neural networks and histograms of oriented graphs. 2021 IEEE 4th International Conference on Advanced Information and Communication Technologies, AICT 2021 Proceedings, 288-292. https://doi.org/10.1109/AICT52120.2021.9628932.
10. Njegovanovi, A. (2018). Digital Financial Decision With A View Of Neuroplasticity/Neurofinancy/Neural Networks.Financial Markets, Institutions and Risks, 2(4),82-91. https://doi.org/10.21272/fmir.2(4).82-91.2018.
11. Yerdon, V.A., Lin, J., Wohleber, R.W., Matthews, G., Reinerman-Jones, L., & Hancock, P.A. (2021). Eye-Tracking Active Indicators of Insider Threats: Detecting Illicit Activity During Normal Workflow. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3059240.
12. Mousa, M., Sai, A.A., & Salhin, G. (2017). An Exploration for the Motives behind Enhancing Senior Bankers Level of Organizational Resilience: A Holistic Case Study. Journal of Intercultural Management, 9(4). https://doi.org/10.1515/joim-2017-0025.
13. Andreou, P.C., & Anyfantaki, S. (2021). Financial literacy and its influence on internet banking behavior. European Management Journal, 39(5). https://doi.org/10.1016/j.emj.2020.12.001.
14. Kuzior, A., Kettler, K., & Rb, . (2022). Digitalization of work and human resources processes as a way to create a sustainable and ethical organization. Energies, 15(1). https://doi.org/10.3390/en15010172.
15. Kirichenko, L., Radivilova, T., & Anders, C. (2017). Detecting cyber threats through social network analysis: short survey. SocioEconomic Challenges, 1(1),20-34.http://doi.org/10.21272/sec.2017.1-03.
16. Carlton, M., Levy, Y., & Ramim, M. (2019). Mitigating cyber attacks through the measurement of non-IT professionals cybersecurity skills. Information and Computer Security, 27(1). https://doi.org/10.1108/ICS-11-2016-0088.
17. Tiutiunyk, I., Kuznetsova, A., & Spankova, J. (2021). Innovative approaches to the assessment of the impact of the shadow economy on social development: an analysis of causation. Marketing and Management of Innovations, 3, 165-174.http://doi.org/10.21272/mmi.2021.3-14.
18. Kaur, J., & Madan, N. (2015). Association Rule Mining: A Survey.International Journal of Hybrid Information Technology,8(7), 239-242. https://doi.org/10.14257/ijhit.2015.8.7.22.
19. Kaur, C. (2013). Association Rule Mining using Apriori Algorithm: A Survey.International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),2(6), 2081-2084.
20. X-Force Threat Intelligence Index 2021. IBM Security (n.d.). Retrieved from: https://www.ibm.com/downloads/cas/M1X3B7QG.
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