Бабенко Михаил Григорьевич

Родился 5 марта 1985 года. Окончил Ставропольский государственный университет в 2007 году. Защитил кандидатскую диссертацию в 2011 г. 

Занимаемые должности: преподаватель кафедры прикладной математики и математического моделирования 2011-по настоящее время.

Член научной школы проф. Червякова Н.И. «Нейроматематика, модулярные нейрокомпьютеры и высокопроизводительные вычисления».

Подготовлено 79 научных публикаций, получено более 6 патентов, поддержано 4 грантовых заявки, в их числе заявки РФФИ, ФЦП и Стипендия Президента РФ молодым ученым и аспирантам.

Издано 3 монографии.

Стаж работы - 14 лет.

Повышение квалификации:

1. Удостоверение о повышении квалификации АВ№001210 от 4 декабря 2015 г.,  «Реализация образовательной деятельности с применением электронного обучения и дистанционных образовательных технологий», 16 часов, ФГБОУ ВО «Московский государственный университет информационных технологий, радиотехники и электроники».

2. Удостоверение о повышении квалификации ПКСК № 007407 от 27 апреля 2015 г., «Инновации в образовании: организация самостоятельной работы студентов в рамках компетентстной модели выпускника», 36 часов, ФГАОУ ВО «Северо-Кавказский федеральный университет»

3. Удостоверение о повышении квалификации ПКСК № 037126 от 13 апреля 2019 г., «Актуальные вопросы преподавания математических и информационных дисциплин с учетом требований федеральных государственных образовательных стандартов», 72 часа,  ФГАОУ ВО «Северо-Кавказский федеральный университет».

4. Удостоверение о повышении квалификации ПКСК № 038198 от 29 апреля 2019 г., «Современные информационно-коммуникационные технологии в высшем образовании», 72 часа, ФГАОУ ВО «Северо-Кавказский федеральный университет»

• математика
• вероятностные методы оценки состояния информационной безопасности
• прикладная математика
• интеллектуальный анализ данных
• защита информации в распределённых вычислительных сетях

Алгебраические структуры в полях Галуа, модулярная арифметика, нейрокомпьютерные технологии, цифровая обработка сигналов, криптографические методы защиты информации.

Монографии:

1.     Червяков Н.И., Коляда А.А., Ляхов П.А., Бабенко М.Г., Лавриненко И.Н., Лавриненко А.В., Модулярная арифметика и ее приложения в инфокоммуникационных технологиях – М.: «Физматлит», 2017. – 402 с.

2.     Червяков Н.И., Ляхов П.А., Бабенко М.Г., Лавриненко И.Н., Лавриненко А.В.КОМПЬЮТЕРНЫЕ ВЫЧИСЛЕНИЯ НА ОСНОВЕ МОДУЛЯРНОЙ АЛГЕБРЫ: \"Издательско-информационный центр \"Фабула\", 2016, 210 с. 

Статьи в журналах Web of Science и Scopus:

1.     Valuev, G., Valueva, M., Babenko, M., and Tchernykh, A. (2023). Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System | IEEE Journals & Magazine | IEEE Xplore, 11, pp. 2680726819, Web of Science: Q2, Impact Factor: 3.476, Scopus: Q1, SJR: 0.93.

2.     Bezuglova, E. S., Shiryaev, E. M., Babenko, M. G., Tchernykh, A., Pulido-Gaytan, B., Corte ́s-Mendoza J. M. (2023) A Survey on Multi-Cloud Storage Security: Threats and Countermeasures. Journal "Computational Technologies", 28(1), pp. 72–80, Scopus: Q4, SJR: 0.22.

3.     Kuchukov, V., Telpukhov, D., Babenko, M., Mkrtchan, I., Stempkovsky, A., Kucherov, N., Ermakova, T. & Grigoryan, M. (2022). Performance Analysis of Hardware Implementations of Reverse Conversion from the Residue Number System. Applied Sciences, 12(23), 12355. DOI: Journal "Computational Technologies", Web of Science: Q2, Impact Factor: 2.838, Scopus: Q2, SJR 0.51:

4.     Gladkov, A., Kuchukov, V., Babenko, M., Tchernykh, A., Berezhnoy, V., & Drozdov, A. Y. (2022).Modified Error Detection and Localization in the Residue Number System | Programming and Computer Software, 48(8), 598-605. Web of Science: Q4, Impact Factor: 0.801, Scopus: Q3, SJR: 0.37.

5.     Vershkov, N., Babenko, M., Tchernykh, A., Kuchukov, V., Kucherov, N., Kuchukova, N., & Drozdov, A. Y. (2022). Optimization of Artificial Neural Networks using Wavelet Transforms | Programming and Computer Software, 48(6), 376-384. Web of Science: Q4, Impact Factor: 0.801, Scopus: Q3, SJR: 0.37.

6.     Valueva, M., Valuev, G., Babenko, M., Tchernykh, A., & Cortes-Mendoza, J. M. (2022). Method for Convolutional Neural Network Hardware Implementation Based on a Residue Number System | Programming and Computer Software. 48(8), 735-744. Web of Science: Q4, Impact Factor: 0.801, Scopus: Q3, SJR: 0.37.

7.     Babenko, M., Tchernykh, A., Pulido-Gaytan, B., Avetisyan, A., Nesmachnow, S., Wang, X., & Granelli, F. (2022). Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control | MDPI. Mathematics, 10(12), 2006. Web of Science: Q1, Impact Factor: 2.258, Scopus: Q2, SJR: 0.54.

8.     Tchernykh, A., Babenko, M., Shiriaev, E., Pulido-Gaytan, B., Cortés-Mendoza, J. M., Avetisyan, A., Drozdov A. Yu., & Kuchukov, V. (2022). An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges. Computation, 10(2), 17. DOI: 10.3390/computation10020017, Scopus: Q2, SJR:0.32.

9.     Babenko, M., Tchernykh, A., & Kuchukov, V. (2022). Improved Modular Division Implementation with the Akushsky Core Function. Computation, 10(1), 9. DOI: 10.3390/computation10010009, Scopus: Q2, SJR:0.32.

10. Babenko, M., Nazarov, A., Deryabin, M., Kucherov, N., Tchernykh, A., Hung, N. V., Avetisyan, & Toporkov, V. (2022). Multiple Error Correction in Redundant Residue Number Systems: A Modified Modular Projection Method with Maximum Likelihood Decoding. Applied Sciences, 12(1), 463. DOI: 10.3390/app12010463, Web of Science: Q2, Impact Factor: 2.679, Scopus: Q2, SJR: 0.44.

11. Canosa-Reyes, R. M., Tchernykh, A., Cortés-Mendoza, J. M., Pulido-Gaytan, B., Rivera-Rodriguez, R., Lozano-Rizk, J. E., Concepción-Morales, E.R., Castro Barrera, H.E., Barrios-Hernandez, C. J., MedranoJaimes, F., Avetisyan, A., Babenko, M., & Drozdov, A. Y. (2022). Dynamic Performance–Energy Tradeoff Consolidation with Contention-Aware Resource Provisioning in Containerized Clouds. PLoS ONE, 17(1), e0261856. DOI: 10.1371/journal.pone.0261856, Web of Science: Q2, Impact Factor: 3.24, Scopus: Q1, SJR: 0.99.

12. Tchernykh, A., Babenko, M., Avetisyan, A., & Drozdov, A. Y. (2021). En-AR-PRNS: Entropy-Based Reliability for Configurable and Scalable Distributed Storage Systems. Mathematics, 10(1), 84. DOI: 10.3390/math10010084, Web of Science: Q1, Impact Factor: 2.258, Scopus: Q2, SJR: 0.5.

13. Pulido-Gaytan, B., Tchernykh, A., Cortés-Mendoza, J. M., Babenko, M., Radchenko, G., Avetisyan, A., & Drozdov, A. Y. (2021). Privacy-Preserving Neural Networks with Homomorphic Encryption: Challenges And Opportunities. Peer-to-Peer Networking and Applications, 14(3), 1666-1691. DOI: 10.1007/s12083-021-01076-8, Web of Science: Q2, Impact Factor: 3.307, Scopus: Q2, SJR: 0.52

14. Babenko, M., Piestrak, S. J., Chervyakov, N., & Deryabin, M. (2021). The Study of Monotonic Core Functions and Their Use to Build RNS Number Comparators. Electronics, 10(9), 1041. DOI: 10.3390/electronics10091041, Web of Science: Q3, Impact Factor: 2.397, Scopus: Q2, SJR: 0.36.

15. Babenko, M., Nazarov, A., Tchernykh, A., Pulido-Gaytan, B., Cortés-Mendoza, J. M., & Vashchenko, I. (2021). Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding. Programming and Computer Software, 47(8), 839-848. DOI: 10.1134/S0361768821080089, Web of Science: Q4, Impact Factor: 0.936, Scopus: Q3, SJR: 0.29.

16. Vershkov, N., Babenko, M., Tchernykh, A., Pulido-Gaytan, B., Cortés-Mendoza, J. M., Kuchukov, V., & Kuchukova, N. (2021). Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation. Programming and Computer Software, 47(8), 830-838. DOI: 10.1134/S0361768821080272, Web of Science: Q4, Impact Factor: 0.936, Scopus: Q3, SJR: 0.29.

17. Tchernykh, A., Babenko, M., Chervyakov, N., Miranda-López, V., Avetisyan, A., Drozdov, A. Y., RiveraRodriguez, R., Radchenko, G., & Du, Z. (2020). Scalable Data Storage Design for Nonstationary IoT Environment with Adaptive Security and Reliability. IEEE Internet of Things Journal, 7(10), 10171-10188. DOI: 10.1109/JIOT.2020.2981276, Web of Science: Q1, Impact Factor: 9.471, Scopus: Q1, SJR: 2.08.

18. Miranda-López, V., Tchernykh, A., Babenko, M., Avetisyan, A., Toporkov, V., & Drozdov, A. Y. (2020). 2Lbp-RRNS: Two-Levels RRNS with Backpropagation for Increased Reliability and Privacy-Preserving of Secure Multi-Clouds Data Storage. IEEE Access, 8, 199424-199439. DOI: 10.1109/ACCESS.2020.3032655, Web of Science: Q2, Impact Factor: 3.367, Scopus: Q1, SJR: 0.59.

19. Chervyakov, N., Lyakhov, P., Babenko, M., Lavrinenko, I., Deryabin, M., Lavrinenko, A., ... & Kaplun, D. (2020). A Division Algorithm in a Redundant Residue Number System Using Fractions. Applied Sciences, 10(2), 695. DOI: 10.3390/app10020695, Web of Science: Q2, Impact Factor: 2.679, Scopus: Q2, SJR: 0.44.

20. Babenko, M., Deryabin, M., Piestrak, S. J., Patronik, P., Chervyakov, N., Tchernykh, A., & Avetisyan, A. (2020). RNS Number Comparator Based on a Modified Diagonal Function. Electronics, 9(11), 1784. DOI: 10.3390/electronics9111784, Web of Science: Q3, Impact Factor: 2.397, Scopus: Q2, SJR: 0.36.

21. Vershkov, N., Babenko, M., Kuchukov, V., & Kuchukova, N. (2020). Search for the Global Extremum Using the Correlation Indicator for Neural Networks Supervised Learning. Programming and Computer Software, 46(8), 609–618. DOI: 10.1134/S0361768820080265, Web of Science: Q4, Impact Factor: 0.936, Scopus: Q3, SJR: 0.29.

22. Tchernykh, A., Miranda-López, V., Babenko, M., Armenta-Cano, F., Radchenko, G., Drozdov, A. Y., & Avetisyan, A. (2019). Performance Evaluation of Secret Sharing Schemes with Data Recovery in Secured and Reliable Heterogeneous Multi-Cloud Storage. Cluster Computing, 22(4), 1173-1185. DOI: 10.1007/s10586-018-02896-9, Web of Science: Q2, Impact Factor: 1.809, Scopus: Q3, SJR: 0.34.

23. Chervyakov, N., Lyakhov, P., Babenko, M., Nazarov, A., Deryabin, M., Lavrinenko, I., & Lavrinenko, A. (2019). A High-Speed Division Algorithm for Modular Numbers Based on the Chinese Remainder Theorem with Fractions and its Hardware Implementation. Electronics, 8(3), 261. DOI: 10.3390/electronics8030261, Web of Science: Q3, Impact Factor: 2.397, Scopus: Q2, SJR: 0.36.

24. Chervyakov, N., Babenko, M., Tchernykh, A., Kucherov, N., Miranda-López, V., & Cortés-Mendoza, J. M. (2019). AR-RRNS: Configurable Reliable Distributed Data Storage Systems for Internet of Things to Ensure Security. Future Generation Computer Systems, 92, 1080-1092. DOI: 10.1016/j.future.2017.09.061, Web of Science: Q1, Impact Factor: 7.187, Scopus: Q1, SJR: 1.26.

25. Babenko, M., Tchernykh, A., Chervyakov, N., Kuchukov, V., Miranda-López, V., Rivera-Rodriguez, R., Du, Z., & Talbi, E. G. (2019). Positional Characteristics for Efficient Number Comparison Over the Homomorphic Encryption. Programming and Computer    Software, 45(8), 532–543. DOI: 10.1134/S0361768819080115, Web of Science: Q4, Impact Factor: 0.936, Scopus: Q3, SJR: 0.29.

26. Tchernykh, A., Schwiegelsohn, U., Talbi, E. G., & Babenko, M. (2019). Towards Understanding Uncertainty in Cloud Computing with Risks of Confidentiality, Integrity, and Availability. Journal of Computational Science, 36, 100581. DOI: 10.1016/j.jocs.2016.11.011, Web of Science: Q1, Impact Factor: 3.976, Scopus: Q1, SJR: 0.7.

27. Tchernykh, A., Babenko, M., Chervyakov, N., Miranda-López, V., Kuchukov, V., Cortés-Mendoza, J. M., ... & Avetisyan, A. (2018). AC-RRNS: Anti-Collusion Secured Data Sharing Scheme for Cloud Storage. International Journal of Approximate Reasoning, 102, 60–73. DOI: 10.1016/j.ijar.2018.07.010, Web of Science: Q2, Impact Factor: 3.816, Scopus: Q1, SJR: 1.04.

28. Chervyakov, N. I., Lyakhov, P. A., Babenko, M. G., Lavrinenko, I. N., Lavrinenko, A. V., Deryabin, M. A., & Nazarov, A. S. (2018). A New Model to Optimize the Architecture of a Fault-Tolerant Modular Neurocomputer. Neurocomputing, 303, 37–46. DOI: 10.1016/j.neucom.2018.04, Web of Science: Q1, Impact Factor: 5.719, Scopus: Q1, SJR: 1.09.

29. Chervyakov, N. I., Lyakhov, P. A., Babenko, M. G., Lavrinenko, I. N., Lavrinenko, A. V., & Nazarov, A. S. (2018). The Architecture of a Fault-Tolerant Modular Neurocomputer Based on Modular Number Projections. Neurocomputing, 272, 96–107. DOI: 10.1016/j.neucom.2017.06.063, Web of Science: Q1, Impact Factor: 5.719, Scopus: Q1, SJR: 1.09.

30. Nikolay Chervyakov, Mikhail Babenko, Andrei Tchernykh, Nikolay Kucherov, Vanessa Miranda-López, Jorge M. Cortés-Mendoza. AR-RRNS:AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security - ScienceDirect. Future Generation Computer Systems.Elsevier Science, 2017, Impact Factor: 3.997, Q1

31. Chervyakov, N. I., Lyakhov, P. A., Babenko, M. G., Lavrinenko, I. N., Lavrinenko, A. V., & Nazarov, A. S. The architecture of a fault-tolerant modular neurocomputer based on modular number projections - ScienceDirect. Neurocomputing. Elsevier Science, 2017, Impact Factor: 3.317, Q1 =

32. Chervyakov, N. I., Molahosseini, A. S., Lyakhov, P. A., Babenko, M. G., & Deryabin, M. A.Residue-to-binary conversion for general moduli sets based on approximate Chinese remainder theorem: International Journal of Computer Mathematics: Vol 94, No 9 ,2017,94(9), 1833-1849. Taylor & Francis, 2017, Impact Factor 0.971, Q2.

33. Chervyakov, N. I., Lyakhov, P. A., Babenko, M. G., Garyanina, A. I., Lavrinenko, I. N., Lavrinenko, A. V., & Deryabin, M. A. (2016). An efficient method of error correction in fault-tolerant modular neurocomputers - ScienceDirect. Neurocomputing, 205, 32-44.Elsevier Science, 2016, Impact Factor: 3.317, Q1.

34. Andrei Tchernykh, Uwe Schwiegelsohn, El-ghazali Talbi, Mikhail Babenko. Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability - ScienceDirect .Journal of Computational Science. Elsevier 2016, Impact Factor:1.748, Q2, 

35. Mikhail Babenko, Nikolay Kucherov, Andrei Tchernykh, Nikolay Chervyakov, Irina Vashenko. Development of a Control System for Computations in BOINC With Homomorphic Encryption in Residue Number System. BOINC: Fundamental & Applied Science & Technology BOINC:FAST 2017. August 28 - 29, 2017, Petrozavodsk, Republic of Karelia, Russia. CEUR Workshop Proceedings, CEUR-WS.org

36. Andrei Tchernykh, Mikhail Babenko, Nikolay Chervyakov, Jorge M. Cortés-Mendoza, Nikolay Kucherov, Vanessa Miranda-López, Maxim Deryabin, Inna Dvoryaninova, Gleb Radchenko. Towards Mitigating Uncertainty of Data Security Breaches and Collusion in Cloud Computing | IEEE Conference Publication | IEEE Xplore. Proceedings of UCC’17 - 1st International Workshop on Uncertainty in Cloud Computing, in conjunction with 28th International Conference on Database and Expert Systems Applications (DEXA’17) Lyon, France. August 28 - 31, 2017, p. 137-141, IEEE, 2017. 2378-3915/17 $31.00 , DOI 10.1109/DEXA.2017.44 (PDF 118KB).

37. Mikhail Babenko, Nikolay Chervyakov, Andrei Tchernykh, Nikolay Kucherov, Maria Shabalina, Irina Vashchenko, Gleb Radchenko, Daniil Murga. Unfairness Correction in P2P Grids Based on Residue Number System of a Special Form | IEEE Conference Publication | IEEE Xplore. Proceedings of UCC’17 - 1st International Workshop on Uncertainty in Cloud Computing, in conjunction with 28th International Conference on Database and Expert Systems Applications (DEXA’17) Lyon, France August 28 - 31, 2017 p.147-151, IEEE, 2378-3915, 2017, DOI 10.1109/DEXA.2017.46 (PDF 136KB).

Общее количество публикаций – более 130 из них авторских свидетельств и патентов – 40, учебных пособий – 5

Индекс Хирша – 7