EVALUATION OF BLOCKCHAIN NODE SECURITY AGAINST ECLIPSE ATTACKS AND TROJAN MALWARE USING DIFFERENTIAL GAME MODELS
DOI:
https://doi.org/10.28925/2663-4023.2026.32.1150Keywords:
блокчейн-вузол, рівень захищеності, Екліпс-атака, троянське шкідливе програмне забезпечення, модель, кібербезпека, диференціальна гра, диференціальні перетворенняAbstract
Blockchain technology is a modern breakthrough in information systems. It has significantly influenced the banking sector by transforming classical approaches to financial transactions, where banks acted as mandatory intermediaries. Today, blockchain has become a foundation of the digital economy. It is also applied in defense and social domains. The high market value of blockchain assets, such as tokens and cryptocurrencies, has recently attracted strong interest from both governmental actors (often from sanctioned states) and non‑governmental cyber groups, as well as individual hackers seeking illegal profit. To achieve this, they conduct cyberattacks against blockchain nodes and entire networks. Attempts to infect blockchain systems with malicious software are also common. This article focuses on evaluating the security of blockchain nodes against Eclipse attacks and Trojan malware. The study applies differential game models based on Markov chains. This approach allows formalizing the probabilities of blockchain node states under the influence of Eclipse Attacks and Trojan Malware using Kolmogorov-Chapman differential equations. To obtain analytical and numerical estimates of security levels, the article employs a differential game method based on non-Taylor differential transformations developed by academician G. Pukhov. The novelty of the results lies in advancing mechanisms of blockchain cybersecurity by selecting optimal defense strategies for nodes exposed to Eclipse Attacks or Trojan Malware. The obtained security estimates provide a scientific basis for practical recommendations on protecting blockchain technology from other dangerous types of cyberattacks and malicious software.
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uo, H., & Yu, X. (2022). A survey on blockchain technology and its security. Blockchain: Research and Applications, 3(2), 100067. https://doi.org/10.1016/j.bcra.2022.100067
Pessa, A. A. B., Perc, M., & Ribeiro, H. V. (2023). Age and market capitalization drive large price variations of cryptocurrencies. Scientific Reports, 13, 3351. https://doi.org/10.1038/s41598-023-30431-3
Krause, D. S. (2025). The $1.4 billion Bybit hack: Cybersecurity failures and the risks of cryptocurrency deregulation. International Journal of Cryptocurrency Research, 5(1), 52–62. https://doi.org/10.51483/IJCCR.5.1.2025.52-62
Barj, S., & Youjil, A. (2024). Blockchain and cryptocurrency security from a layered perspective using MITRE ATT&CK. International Journal of Engineering Trends and Technology, 72(4), 1–14. https://doi.org/10.14445/22315381/IJETT-V72I4P101
Hassija, V., Zeadally, S., Jain, I., Tahiliani, A., Chamola, V., & Gupta, S. (2021). Framework for determining the suitability of blockchain. Transactions on Emerging Telecommunications Technologies, 32(10), e4334. https://doi.org/10.1002/ett.4334
Aggarwal, S., & Kumar, N. (2021). Attacks on blockchain. In P. Raj (Ed.), Advances in computers (Vol. 121, pp. 399–410). Elsevier. https://doi.org/10.1016/bs.adcom.2020.08.020
Alachkar, K., & Gaastra, D. (2018). Blockchain-based Sybil attack mitigation: A case study of the I2P network. In Proceedings of the International Conference on Network Protocols (ICNP) (pp. 1–13).
Chaganti, R., Boppana, R. V., Ravi, V., Munir, K., Almutairi, M., Rustam, F., Lee, E., & Ashraf, I. (2022). Denial-of-service attacks in blockchain ecosystem: A review. IEEE Access, 10, 96538–96555. https://doi.org/10.1109/ACCESS.2022.3205019
Aghili, S. (2024). Leveraging blockchain technology: Governance, risk, compliance, security, and use cases. CRC Press. https://doi.org/10.1201/9781003462033
Heilman, E., Kendler, A., Zohar, A., & Goldberg, S. (2015). Eclipse attacks on Bitcoin’s peer-to-peer network. In Proceedings of the 24th USENIX Security Symposium (pp. 129–144).
Marcus, Y., Heilman, E., & Goldberg, S. (2018). Low-resource eclipse attacks on Ethereum’s peer-to-peer network. IACR Cryptology ePrint Archive. https://eprint.iacr.org/2018/236
Hryshchuk, R., Yevseiev, S., & Shmatko, A. (2018). Construction methodology of information security systems of banking information. Premier Publishing
Alasmary, W., Alhaidari, F., Alharthi, A., & Alsubhi, K. (2024). Malware trends: Detection and mitigation strategies. Journal of Cybersecurity and Digital Forensics, 6(1), 45–62. https://doi.org/10.1109/JCDF.2024.0006
Hadikosyah, G. A., Zannah, N. F., Yulistia, S., Febrian, M. R., Maulana, F., & Sulthan, R. (2026). Trojan malware propagation and impact. JIKUM: Jurnal Ilmu Komputer, 2(1), 47–52. https://doi.org/10.62671/jikum.v2i1.153
McElroy, S. (2024). Identifying Android banking malware through UI complexity. In IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 348–353). https://doi.org/10.1109/CSR61664.2024.10679403
Reijonen, A. (2024). The evolution of mobile malware (Master’s thesis, JAMK University). https://surl.lu/vicrdm
Zimperium Inc. (2025). 2025 global mobile threat report. https://surl.li/kzmhnq
The Hacker News. (2025). New Android Trojan Crocodilus abuses accessibility. https://thehackernews.com/2025/03/new-android-trojan-crocodilus-abuses.html
Rieck, K., Holz, T., Willems, C., Düssel, P., & Laskov, P. (2008). Malware behavior classification. In DIMVA 2008 (pp. 108–127). https://doi.org/10.1007/978-3-540-70542-0_6
Hryshchuk, R. (2021). Differential transformations in cybersecurity. In CEUR Workshop Proceedings (Vol. 3200, pp. 223–227).
Hryshchuk, R., & Korchenko, O. (2012). Differential game models of cyber attacks. Ukrainian Information Security Research Journal, (3), 115–122. https://doi.org/10.18372/2410-7840.14.3418
Myerson, R. B. (1991). Game theory: Analysis of conflict. Harvard University Press
Stifter, N., Judmayer, A., Schindler, P., Zamyatin, A., & Weippl, E. R. (2018). Formalization of Nakamoto consensus. IACR Cryptology ePrint Archive. https://eprint.iacr.org/2018/400
Liu, Z., Luong, N. C., Wang, W., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2019). Blockchain from game theory perspective. IEEE Access, 7, 47615–47643. https://doi.org/10.1109/ACCESS.2019.2909924
Zhang, Z. (2019). Engineering token economy. arXiv. https://doi.org/10.48550/arXiv.1907.00899
Zhang, Z., Zargham, M., & Preciado, V. M. (2020). Blockchain-enabled economic networks. Applied Network Science, 5, 19. https://doi.org/10.1007/s41109-020-0254-9
Wang, H., & An, J. (2023). Game-based blockchain security. The Journal of Supercomputing, 79, 15894–15926. https://doi.org/10.1007/s11227-023-05289-x
Zhiyong, L., Shuyi, W., Weiwei, S., Jiahui, L., & Jianming, W. (2023). Blockchain security situation awareness. Journal of China Universities of Posts and Telecommunications, 30(4), 105–120. https://doi.org/10.19682/j.cnki.1005-8885.2023.2020
Zhou, C., Xing, L., Liu, Q., & Wang, H. (2021). Bitcoin reliability under eclipse attacks. IJMEMS, 6(2), 480–492. https://doi.org/10.33889/IJMEMS.2021.6.2.029
Zhou, C., Xing, L., Guo, J., & Liu, Q. (2022). Bitcoin selfish mining analysis. IJMEMS, 7(1), 16–27.
Zhou, C., Xing, L., Liu, Q., & Li, Y. (2023). Bitcoin dependability under attacks. IJMEMS, 8(4), 547–559.
Zhou, C., Xing, L., Liu, Q., & Wang, H. (2023). Defense strategies for selfish mining. Applied Sciences, 13(1), 422. https://doi.org/10.3390/app13010422
del Rey, A. M. (2015). Malware propagation modeling. Security and Communication Networks, 8(15), 2561–2579. https://doi.org/10.1002/sec.1186
Karyotis, V., & Khouzani, M. (2016). Malware diffusion models. Morgan Kaufmann
Liu, Q. (2021). Security risk assessment (Doctoral dissertation). https://doi.org/10.62791/19801
Fang, Z., Zhao, P., Xu, M., Xu, S., Hu, T., & Fang, X. (2022). Statistical malware modeling. Journal of Applied Statistics, 49(4), 858–883.
Signes-Pont, M. T., Castillo, A. C., Mora, H. M., & Szymanski, J. (2018). Mobile malware propagation. Computers & Security. https://doi.org/10.1016/j.cose.2018.08.004
Quiroga-Sánchez, L., Montoya, G., & Lozano-Garzon, C. (2025). Malware propagation in IoT. Cybersecurity, 8, 2.
Pappu, K., et al. (2025). Malware propagation dynamics. arXiv.
Omar, O. A. M., et al. (2025). Malware propagation in cloud systems. Mathematics and Computers in Applications, 30(1), 8.
Zhou, Y., et al. (2023). Epidemic models for malware. Frontiers in Physics, 11, 1198410.
Hryshchuk, R. V. (2010). Theoretical foundations of cyber attack modeling. Ruta
Pukhov, G. E. (1978). Differential transformations. Cybernetics and Systems Analysis, 14, 383–390
Stasiuk, O. I., & Baranov, H. V. (2006). Differential transformations for computer modeling.
ISO/IEC. (2022). Information security risk management (ISO/IEC 27005:2022). https://www.iso.org/standard/80585.html
Maplesoft. (2025). Maple for students. https://www.maplesoft.com/products/Maple/student.aspx
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