Electronic footprint analysis and cluster analysis techniques for information security risk research of university digital systems

• Autorzy: Lakhno Valerii , Lakhno Myroslav , Makulov Kaiyrbek , Kryvoruchko Olena , Desiatko Alona , Chubaievskyi Vitalii , Ishchuk Dmytro , Kabylbekova Viktoriya

Identyfikatory

DOI

10.24425/ijet.2024.149559

• Języki publikacji: EN

Abstrakty

EN

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university's DEE.

Słowa kluczowe

EN

cluster analysis; digital footprints; hierarchical classification; k-means method; information security; risks; university digital educational environment

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 2
• Strony: 405--412
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Lakhno Valerii

• National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

autor

Lakhno Myroslav

• National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

autor

Makulov Kaiyrbek

• Caspian University of Technology and Engineering named after Sh. Yesenova, Almaty, Kazakhstan

autor

Kryvoruchko Olena

• State University of Trade and Economics, Kyiv, Ukraine

autor

Desiatko Alona

• State University of Trade and Economics, Kyiv, Ukraine

autor

Chubaievskyi Vitalii

• State University of Trade and Economics, Kyiv, Ukraine

autor

Ishchuk Dmytro

• Zhytomyr Politechnic State University, Zhytomyr, Ukraine

autor

Kabylbekova Viktoriya

• Caspian University of Technology and Engineering named after Sh. Yesenova, Almaty, Kazakhstan

Bibliografia

• [1] Oliveira, P. C. D., Cunha, C. J. C. D. A., & Nakayama, M. K. (2016). Learning Management Systems (LMS) and e-learning management: an integrative review and research agenda. JISTEM-Journal of Information Systems and Technology Management, 13, 157-180. https://doi.org/10.4301/S1807-17752016000200001.
• [2] Aldiab, A., Chowdhury, H., Kootsookos, A., Alam, F., & Allhibi, H. (2019). Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia. Energy Procedia, 160, 731-737. https://doi.org/10.1016/j.egypro.2019.02.186.
• [3] Azcona, D., Hsiao, IH. & Smeaton, A.F. Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints. User Model User-Adap Inter 29, 759-788 (2019). https://doi.org/10.1007/s11257-019-09234-7.
• [4] Nai, R., Sulis, E., Marengo, E., Vinai, M., Capecchi, S. (2023). Process Mining on Students’ Web Learning Traces: A Case Study with an Ethnographic Analysis. In: Viberg, O., Jivet, I., Muñoz-Merino, P., Perifanou, M., Papathoma, T. (eds) Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200. Springer, Cham. https://doi.org/10.1007/978-3-031-42682-7_48.
• [5] Mohssine, B., Mohammed, A., Abdelwahed, N., & Mohammed, T. (2021). Adaptive help system based on learners 'digital traces' and learning styles. International Journal of Emerging Technologies in Learning (IJET), 16(10), 288-294. https://doi.org/10.3991/ijet.v16i10.19839.
• [6] Ye, D., & Pennisi, S. (2022). Using trace data to enhance students' self-regulation: A learning analytics perspective. The Internet and Higher Education, 54, 100855. https://doi.org/10.1016/j.iheduc.2022.100855.
• [7] Noskova, T., Pavlova, T., & Yakovleva, O. (2018). Study of students' educational activity strategies in the social media environment. E-learning and Smart Learning Environment for the Preparation of New Generation Specialists, 10, 113-125.
• [8] N. Kadoić and D. Oreški, "Analysis of student behavior and success based on logs in Moodle," 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2018, pp. 0654-0659, https://doi.org/10.23919/MIPRO.2018.8400123.
• [9] Mogus, A. M., Djurdjevic, I., & Suvak, N. (2012). The impact of student activity in a virtual learning environment on their final mark. Active Learning in Higher Education, 13(3), 177-189. https://doi.org/10.1177/1469787412452985.
• [10] Stiller, K., & Bachmaier, R. (2018, June). Identifying learner types in distance training by using study times. In EDEN Conference Proceedings (No. 1, pp. 78-86). https://doi.org/10.38069/edenconf-2018-ac-0012.
• [11] Ahmed, A. I., Alharthe, R. M., & Alfereej, M. M. (2023). Organizational committees and their role in enhancing intellectual security: a case study on female students of the Bachelor of Information Science program-College of Arts-Imam Abdul Rahman bin Faisal University. Library Philosophy and Practice, 1-26.
• [12] Cheung, S.K.S. (2014). Information Security Management for Higher Education Institutions. In: Pan, J. S., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_2.
• [13] Al Quhtani, M. (2017). Data mining usage in corporate information security: Intrusion detection applications. Business Systems Research: International Journal of the Society for Advancing Innovation and Research in Economy, 8(1), 51-59. https://doi.org/10.1515/bsrj-2017-0005.
• [14] Salem, I. E., Mijwil, M. M., Abdulqader, A. W., Ismaeel, M. M., Alkhazraji, A., & Alaabdin, A. M. Z. (2022). Introduction to The Data Mining Techniques in Cybersecurity. Mesopotamian journal of cybersecurity, 2022, 28-37. https://doi.org/10.58496/MJBD/2023/007.
• [15] Kong, J., Yang, C., Wang, J., Wang, X., Zuo, M., Jin, X., & Lin, S. (2021). Deep-stacking network approach by multisource data mining for hazardous risk identification in IoT-based intelligent food management systems. Computational Intelligence and Neuroscience, Volume 202, Article ID 1194565. https://doi.org/10.1155/2021/1194565.
• [16] Mathew, A. (2023). The Power of Cybersecurity Data Science in Protecting Digital Footprints. Cognizance Journal of Multidisciplinary Studies, Vol.3, Issue.2, February 2023, pg. 1-4. https://doi.org/10.47760/cognizance.2023.v03i02.001.
• [17] Muhammad, S. S., Dey, B. L., & Weerakkody, V. (2018). Analysis of factors that influence customers' willingness to leave big data digital footprints on social media: A systematic review of literature. Information Systems Frontiers, 20, 559-576. https://doi.org/10.1007/s10796-017-9802-y.
• [18] Cheng, F. C., & Wang, Y. S. (2018). The do not track mechanism for digital footprint privacy protection in marketing applications. Journal of Business Economics and Management, 19(2), 253-267. https://doi.org/10.3846/jbem.2018.5200.
• [19] Bollé, T., & Casey, E. (2018). Using computed similarity of distinctive digital traces to evaluate non-obvious links and repetitions in cyber-investigations. Digital Investigation, 24, S2-S9. https://doi.org/10.1016/j.diin.2018.01.002.
• [20] Sarini, Marcello, Rossana Actis Grosso, Maria Elena Magrin, Silvia Mari, Nadia Olivero, Giulia Paganin, and Silvia Simbula. 2022. "A Cluster Analysis of the Acceptance of a Contact Tracing App - The Identification of Profiles for the Italian Immuni Contact Tracing App" Healthcare 10, no. 5: 888. https://doi.org/10.3390/healthcare10050888.
• [21] Romesburg, C. (2004). Cluster analysis for researchers. Lulu. com. 334 pp.