DOI: 10.17587/prin.14.493-501

A Study of Existing Approaches to Transaction Analysis in the Bitcoin Network

E. A. Basinya, Associate Professor, Leading Researcher, eabsynya@mephi.ru, N. Karapetyants, Assistant of Department, nkarapetyants@mephi.ru, M. Karapetyants, IICS Engineer, mkarapetyants@mephi.ru, National Research Nuclear University MEphl, Moscow, 115409, Russian Federation

Corresponding author: Evgeniy A. Basynya, Associate Professor, Leading Researcher, National Research Nuclear University "MEPhI", Moscow, 115409, Russian Federation, E-mail: eabsynya@mephi.ru

Today, the Bitcoin network faces a number of challenges, such as flawed user identification and fraudulent transaction methods that are used by criminals to conduct illegal activities. As a result, there is a growing need to improve existing tools for tracking transactions, as well as to develop new methods for identifying money in the Bitcoin network. The paper presents a study and systematization of the subject area problems, and also considers possible approaches to neutralize it. The purpose of this work is to analyze the existing methods of transaction verification of the Bitcoin network. Within the framework of the work, a system architecture is proposed, which includes a comprehensive approach to the process of transaction analysis. Each of the stages of this process is described: information gathering, aggregation, processing and analysis. An extended set of empirical rules (heuristics) for transaction analysis, which are used in existing clustering methods, is considered. The results of this work will provide an opportunity to improve the existing Bitcoin transaction verification methods and develop a new one with the possibility of increasing the efficiency of the process of identification of illegally obtained funds and their sources.

Keywords: blockhain, Bitcoin, KYC, KYT, transaction analysis, clusterization, heuristic

pp. 493–501

For citation:
Basynya E. A., Karapetyants N., Karapetyants M. A Study of Existing Approaches to Transaction Analysis in the Bitcoin Network, Programmnaya Ingeneria, 2023, vol. 14, no. 10, pp. 493—501. DOI: 10.17587/prin.14.492-501 (in Russian).

This work was supported by the Ministry of Science and Higher Education of the Russian Federation (state task project No. FSWU-2023-0031).

References:

• Chainalysis — The 2022 Crypto Crime Report, Chainalysis, accessed 2 February 2023, available at: https://blockbr.com.br/wp-content/uploads/2022/06/2022-crypto-crime-report.pdf.
• Serdechny A. L., Skogoreva D. A., Longny E. P. et al. Cartographic study of blockchain transactions and smart contracts of cybercriminals attacking automated information systems and assessment of damages from the implementation of their attacks, Informacija i bezopasnost', 2021, vol. 24, no. 4, pp. 471—500. DOI: 10.36622/VSTU.2021.24.4.001 (in Russian).
• Rodivilina V. A., Rodivilin I. P., Kolominov V. V. Problems of countering the use of anonymity on the Internet for criminal purposes, Kriminalistika: vchera, segodnja, zavtra, 2021, no. 4, pp. 68—79. DOI: 10.24412/2587-9820-2021-4-68-76 (in Russian).
• Balaskas A., Frankeira V. N. L. Analytical tools for block-chain: Review, taxonomy and open challenges, 2018 International conference on cybersecurity and protection of digital services (cybersecurity), IEEE, 2018, pp. 1—8. DOI: 10.1109/CyberSecPODS.2018.8560672.
• Song W., Zhang W., Wang J. et al. Blockchain data analysis from the perspective of complex networks: Overview, Tsinghua Science and Technology, 2022, vol. 28, no. 1, pp. 176—206. DOI: 10.26599/TST.2021.9010080.
• Moon H., Kim S., Li Y. A RDBMS-based Bitcoin analysis method, Information Security and Cryptology — ICISC, 2020, vol. 12593, pp. 235—253. DOI: 10.1007/978-3-030-68890-5_13.
• Bakumenko L. P., Vasilyeva N. S. Classification by the sup­port vector method of bitcoin theft fraud programs, Uchet i statistika, 2022, vol. 68, no. 4, pp. 112—122 (in Russian).
• Feldman E. V., Ruchai A. N., Matveeva V. K., Samsono-va V. D. A model for detecting anomalous bitcoin transactions based on machine learning, Cheljabinskij fiziko-matematicheskij zhurnal, 2021, vol. 6, no. 1, pp. 119—132. DOI: 10.47475/2500-0101-2021­16110 (in Russian).
• Zheng B., Zhu L., Shen M. Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering, Science China Information Sciences, 2020, vol. 63, pp. 1—15. DOI: 10.1007/s11432-019-9900-9.
• He S., He K., Lin S., Yang C., Mao H. Bitcoin address clustering method based on multiple heuristic condition, IET Blockchain, 2022, vol. 2, no. 2, pp. 44—56. DOI: 10.1049/blc2.12014.
• Long T., Xu J., Fu L., Wang X. Analyzing and de-anon-ymizing Bitcoin networks: An IP matching method with clustering and heuristics // China Communications, 2022, vol. 19, no. 6, pp. 263—278. DOI: 10.23919/JCC.2022.06.019.
• Moser M., Narayanan A. Resurrecting address clustering in Bitcoin, Financial Cryptography and Data Security: 26th International Conference, 2022, vol. 13411, pp. 386—403. DOI: 10.1007/978-3-031-18283-9_19
• Zhao Z., Wang J., Shi Q., Zhang H. Improving Bitcoin Address Clustering by Proposing Heuristics, IEEE Transactions on Network and Service Management, 2022, vol. 19, no. 4, pp. 3737—3749. DOI: 10.1109/TNSM.2022.3186466.
• Chang T. H., Svetinovich D. Improving bitcoin ownership identification using transaction patterns analysis, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, vol. 50, no. 1, pp. 9—20. DOI: 10.1109/TSMC.2018.2867497.
• Fisher J. A., Palechor A., Dell'Aglio D., Bernstein A., Tesson C. J. The complex community structure of the bitcoin address correspondence network, Frontiers in Physics, 2021, vol. 9, pp. 1—16. DOI: 10.3389/fphy.2021.681798.
• Traag V. A. Faster Community Deployment: Accelerating the Louvain Algorithm, Physical Review E, 2015, vol. 92, no. 3, pp. 032801. DOI: 10.1103/PhysRevE.92.032801.
• Traag V. A., Shubel L. Detection of a large network community by fast label propagation, Scientific report, 2023, vol. 13, pp. 2701. DOI: 10.1038/s41598-023-29610-z