Approach to development of a system for detecting incidents of information security of information resources of banking systems, when implementing stages of counteraction of illegal actions
DOI:
https://doi.org/10.52575/2687-0932-2021-48-1-116-122Keywords:
information security, banking information resources, identification of information security incidents, correlation rules, information security incident detection systemAbstract
The purpose of this article is to consider an approach to determining the probability of detecting information security incidents of information resources of banking systems, when implementing the stages of countering illegal actions (unauthorized actions, copying, changing, destroying information). The authors considered the relevance of using the Security Information and Event Management (SIEM) system. The data sources for incident detection systems, attributes that can be analyzed by the SIEM system are described. When considering an approach to determining the probability of detecting information security incidents, the parameters vn, v (min) were introduced, denoting the volume and minimum volume of the correlation rule base of the information security incident detection system, respectively. As a result, the probability was determined, which makes it possible to fully characterize the system for identifying incidents of information security of information resources of banking systems when implementing the stages of countering illegal actions (unauthorized actions, copying, changing, destroying information).
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Ефремова О.А., Никитин В.М., Чурносов М.И., Камышникова Л.А., Липунова Е.А., Муромцев В.В. 2016. Виртуальный способ оценки риска развития ишемической болезни сердца у носителей полиморфных кардиогенов. Научные ведомости Белгородского государственного университета. Серия «Медицина. Фармация», 26 (247): 76–83.
Муромцев В.В., Никитин В.М., Ефремова О.А., Камышникова Л.А. 2019. Подход к улучшению автоматизированной системы компьютерного анализа электрокардиограммы. Медицинские технологии. Оценка и выбор, 2 (36): 42–48.
Обухов С.А., Степанов В.П. 2019. Алгоритм обнаружения QRS-комплекса на электрокардиограмме в реальном времени. Инженерный журнал: наука и инновации, 5. [Электронный ресурс] URL: http://dx.doi.org/10.18698/2308-6033-2019-5-1877. (дата обращения
декабря 2020).
Петров С.П., Епишина Е.В., Воронин В.В. 2014. Оценка алгоритмов распознавания образов для задач автоматического анализа электрокардиограмм. Евразийский союз ученых,
(8–8): 27–29. [Электронный ресурс] URL: https://euroasia-science.ru/wp-content/uploads/2016/11/ evro_8p8_6-169.pdf (дата обращения 12 декабря 2020).
Рослякова А.В., Чупраков П.Г. 2012. Сравнительный анализ алгоритмов обнаружения
R-зубца Электрокардиосигнала. Вятский медицинский вестник, 2: 29–35.
Черемных С.В., Семенов И.О., Ручкин В.С. 2001. Структурный анализ систем:
IDEF-технологии. М., Финансы и статистика, 208.
Al-Naymat G., Chawla S., Taheri J. 2012. SparseDTW: A Novel Approach to Speed up Dynamic Time Warping arXiv:1201.2969v1 [cs.DB]. Available at: https://arxiv.org/pdf/1201.2969v1.pdf (accessed 12 December 2020).
Ansari S., Farzaneh N., Duda M., Horan K., Andersson H.B. 2017. Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Reviews in Biomedical Engineering, 10: 264–298.
Choi E., Bahadori M.T., Schuetz A., Stewart W.F., Sun J. 2016. Doctor AI: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference. 56: 301–318.
Fernandez Biscay C., Arini P.D., Rincón Soler A.I. et al. 2020. Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet transform. Medical & Biological Engineering & Computing, 58, 1069–1078 [Electronic resource] Available at: https://doi.org/10.1007/s11517-020-02134-8. (accessed 12 December 2020).
LabVIEW. National Instruments. 2020. [Electronic resource] Available at: http://www.ni.com/. (accessed 12 December 2020).
Muromtsev V.V., Nikitin V.M., Efremova O.A., Kamyshnikova L.A., Ushakova S.N. 2020. One of the approaches to automating the analysis of the shape of ECG sections International Journal of Advanced Research in Engineering and Technology (IJARET), 11(7): 179–186.
Pan J., Tompkins W.J. 1985. A real time QRS detection algorithm. IEEE transactions on Biomedical Engineering, 32: 230–236.
PhysioBank. Databases. 2020. [Electronic resource]. Available at: http://physionet.org/physiobank/database/ (accessed 12 December 2020).
Sandau K.E., Funk M., Auerbach A., Barsness G.W., Blum K., Cvach M., Lampert R., May J.L., McDaniel G.M., Perez MV. 2017. Update to practice standards for electrocardiographic monitoring in hospital settings: a scientific statement from the American heart association. Circulation, 136 (19): e273–e344.
Steinhubl S.R., Waalen J., Edwards A.M., et al. 2018. Effect of a Home-Based Wearable Continuous ECG Monitoring. Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA, 320 (2): 146–155.
Surawicz B., Knilans T. 2008. Chou’s Electrocardiography in Clinical Practice. 6th Edition. Saunders, 752.
Visio. 2020. [Electronic resource]. Available at: https://www.microsoft.com/ru-ru/microsoft-365/visio/flowchart-software. (accessed 12 December 2020).
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