A Comprehensive Approach to Ensuring Business Continuity Based on Centralized Data Backup Systems
https://doi.org/10.31854/1813-324X-2026-12-1-16-25
EDN: YTOMOZ
Abstract
Relevance. Modern business processes critically depend on the continuous availability of data, while the frequency of data loss incidents due to ransomware attacks, equipment failures, and administrative errors is steadily increasing.
The purpose of the study is to develop and validate a comprehensive approach to building centralized data backup systems that minimize recovery time and maximize the likelihood of successful recovery from catastrophic failures of various types.
Methods. The methodological basis of this study is a systems approach to backup architecture design, quantitative risk analysis methods, and comparative analysis.
Solution (Results). A comprehensive architectural approach based on a centralized pull-oriented backup system has been developed. A methodology for choosing between full disk backup and granular file backup has been formulated. The scientific novelty lies in the justification of a comprehensive centralized data backup system that combines snapshot-oriented backup, a multi-tiered storage architecture, and a pull model with a physically and logically isolated server.
The theoretical significance lies in the development of a scientific and methodological framework for ensuring business process continuity by formalizing criteria for selecting backup modes and architectural solutions, taking into account resource limitations and data heterogeneity.
The practical significance of the work lies in the development of specific architectural requirements and recommendations for building backup systems.
About the Authors
S. S. SokolovRussian Federation
O. S. Lauta
Russian Federation
M. V. Mitrofanov
Russian Federation
A. S. Kurakin
Russian Federation
N. N. Kramskoy
Russian Federation
References
1. Hou Y., Guo L., Zhou C., Xu Y., Yin Z., Li S., et al. An Empirical Study of Data Disruption by Ransomware Attacks. Proceedings of the 46th International Conference on Software Engineering, ICSE ’24, 14‒20 April 2024, Lisbon, Portugal. New York: Association for Computing Machinery; 2024. p.161. DOI:10.1145/3597503.3639090
2. Al-rimy B.A.S., Maarof M.A., Shaid S.Z.M. Ransomware threat success factors, taxonomy, and countermeasures: A survey and research directions. Computers & Security. 2018;74:144‒166. DOI:10.1016/j.cose.2018.01.001
3. Kotenko I.V., Saenko I.B., Lauta O.S., Vasiliev N.A., Sadovnikov V.E. A Method of countering adversarial attacks on image classification systems. Cybersecurity Issues. 2025;2(66):114‒123. (in Russ.) DOI:10.21681/2311-3456-2025-2-114-123. EDN:MKWZFT
4. Hou Y., Guo L., Zhou C., Zhang Q., Liu W., Sun C., et al. Preventing Disruption of System Backup against Ransomware Attacks. Proceedings of the ACM on Software Engineering. 2025;2(ISSTA):229‒249. DOI:10.1145/3728880. EDN:XZVFZD
5. Zhou C., Guo L., Hou Y., Ma Z., Zhang Q., Wang M. Limits of I/O Based Ransomware Detection: An Imitation Based Attack. Proceedings of the Symposium on Security and Privacy, SP, 21‒25 May 2023, San Francisco, USA. IEEE; 2023. DOI:10.1109/SP46215.2023.10179372
6. Kolodenker E., Koch W., Stringhini G., Egele M. PayBreak: Defense Against Cryptographic Ransomware. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, ASIACCS, 2‒6 April 2017, Abu Dhabi, United Arab Emirates. New York: Association for Computing Machinery; 2017. p.599‒611. DOI:10.1145/3052973.3053035
7. Nikolaev V.V., Saenko I.B. Optimization of Information Resources Distribution in Common Information Space. Proceedings of Telecommunication Universities. 2024;10(3):87‒103. (in Russ.) DOI:10.31854/1813-324X-2024-10-3-87-103. EDN:UFROMW
8. Hirano M., Hodota R., Kobayashi R. RanSAP: An open dataset of ransomware storage access patterns for training machine learning models. Forensic Science International: Digital Investigation. 2021;40:301314 DOI:10.1016/j.fsidi.2021.301314. EDN:OZNYCW
9. Hirano M., Kobayashi R. RanSMAP: Open dataset of Ransomware Storage and Memory Access Patterns for creating deep learning based ransomware detectors. Computers & Security. 2025;150:104202. DOI:10.1016/j.cose.2024.104202. EDN:GUQFQK
10. Min D., Park Y., Yoon S., Walker R., Lee J., Park S. Amoeba: An Autonomous Backup and Recovery SSD for Ransomware Attack Defense. IEEE Computer Architecture Letters. 2018;17(2):245‒248. DOI:10.1109/LCA.2018.2883431
11. Ilau M.-C., Baldwin A., Caulfield T., Pym D. Modelling and simulating organizational ransomware recovery: structure, methodology, and decisions // Journal of Cybersecurity. 2025;11(1). DOI:10.1093/cybsec/tyaf035
12. Kritika Er. A comprehensive literature review on ransomware detection using deep learning techniques. Cyber Security and Applications. 2025;3:100078. DOI:10.1016/j.csa.2024.100078
Review
For citations:
Sokolov S.S., Lauta O.S., Mitrofanov M.V., Kurakin A.S., Kramskoy N.N. A Comprehensive Approach to Ensuring Business Continuity Based on Centralized Data Backup Systems. Proceedings of Telecommunication Universities. 2026;12(1):16-25. (In Russ.) https://doi.org/10.31854/1813-324X-2026-12-1-16-25. EDN: YTOMOZ
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