Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning

  • Ferdiansyah Ferdiansyah Universitas Indo Global Mandiri, Indonesia
  • Usman Ependi Bina Darma University, Indonesia
  • Tasmi Tasmi Universitas Indo Global Mandiri, Indonesia
  • Muhammad Haikal Universitas Indo Global Mandiri, Indonesia
  • Mikko Mikko Universitas Indo Global Mandiri, Indonesia
DOI: 10.51519/journalisi.v7i1.1047
Keywords: Blockchain Security, Anomaly Detection, Machine Learning, Fraud Detection

Abstract

Blockchain technology has revolutionized data security and transaction transparency across various industries. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. This study aims to analyze anomalies in blockchain systems using machine learning approaches. Various anomaly detection techniques, including supervised and unsupervised methods, are evaluated for their effectiveness in identifying irregularities. The results indicate that   machine learning models can detect anomalies with high accuracy, providing insights into potential threats and system vulnerabilities. The findings of this research contribute to improving blockchain security and developing more robust monitoring systems.

Downloads

Download data is not yet available.

References

Z. Liu, H. Gao, H. Lei, Z. Liu, and C. Liu, “Blockchain anomaly transaction detection: An overview, challenges, and open issues,” in Int. Conf. Inf. Sci., Commun. Comput., 2023, pp. 126–140.

E. P.-E. George, C. Idemudia, and A. B. Ige, “Blockchain technology in financial services: Enhancing security, transparency, and efficiency in transactions and services,” Open Access Res. J. Multidiscip. Stud., 2024, doi: 10.53022/oarjms.2024.8.1.0042

A. Judmayer, N. Stifter, K. Krombholz, and E. Weippl, Blocks and Chains: Introduction to Bitcoin, Cryptocurrencies, and Their Consensus Mechanisms. Synth. Lect. Inf. Secur. Priv. Trust, 2017, doi: 10.2200/s00773ed1v01y201704spt020.

K. Croman et al., “On scaling decentralized blockchains: (A position paper),” in Int. Conf. Financial Cryptography Data Secur., 2016, pp. 106–125.

Y. Ikeda, R. Hadfi, T. Ito, and A. Fujihara, “Anomaly detection and facilitation AI to empower decentralized autonomous organizations for secure crypto-asset transactions,” AI Soc., pp. 1–12, 2025.

Ł. Apiecionek and P. Karbowski, “Fuzzy neural network for detecting anomalies in blockchain transactions,” Electronics, vol. 13, no. 23, p. 4646, 2024.

G. S. Rai, S. B. Goyal, and P. Chatterjee, “Anomaly detection in blockchain using machine learning,” in Comput. Intell. Eng. Manag. Appl.: Sel. Proc. CIEMA 2022, Springer, 2023, pp. 487–499.

J. Bonneau et al., “SOK: Research perspectives and challenges for bitcoin and cryptocurrencies,” in Proc. 2015 IEEE Symp. Secur. Privacy, 2015, pp. 104–121.

M. Hasan, M. S. Rahman, H. Janicke, and I. H. Sarker, “Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis,” Blockchain Res. Appl., vol. 5, no. 3, p. 100207, 2024.

S. Siddamsetti, C. Tejaswi, and P. Maddula, “Anomaly detection in blockchain using machine learning,” J. Electr. Syst., vol. 20, pp. 619–634, 2024.

M. T. R. Laskar et al., “Extending isolation forest for anomaly detection in big data via K-means,” ACM Trans. Cyber-Phys. Syst., vol. 5, no. 4, pp. 1–26, 2021.

A. B. Nassif, M. A. Talib, Q. Nasir, and F. M. Dakalbab, “Machine learning for anomaly detection: A systematic review,” IEEE Access, vol. 9, pp. 78658–78700, 2021.

X. Ugarte-Pedrero et al., “On the adoption of anomaly detection for packed executable filtering,” Comput. Secur., vol. 43, pp. 126–144, 2014, doi: 10.1016/j.cose.2014.03.012.

J. Akoto and T. Salman, “Machine learning vs deep learning for anomaly detection and categorization in multi-cloud environments,” in Proc. 2022 IEEE Cloud Summit, 2022, pp. 44–50.

M. Conti, E. S. Kumar, C. Lal, and S. Ruj, “A survey on security and privacy issues of bitcoin,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3416–3452, 2018.

A. Kiayias, A. Russell, B. David, and R. Oliynykov, “Ouroboros: A provably secure proof-of-stake blockchain protocol,” in Annu. Int. Cryptol. Conf., 2017, pp. 357–388.

Q. Kong, H. Gong, X. Ding, and R. Hou, “Classification application based on mutual information and random forest method for high dimensional data,” in 2017 9th Int. Conf. Intell. Human-Machine Syst. Cybern. (IHMSC), 2017, pp. 171–174.

M. Mehrotra and N. Joshi, “Anomaly detection in temporal data using KMeans clustering with C5.0,” Int. J. Eng. Sci., vol. 6, no. 5, pp. 77–81, 2017.

A. Sreenivasulu, “Evaluation of cluster based anomaly detection,” 2019.

D. R. Cutler et al., “Random forests for classification in ecology,” Ecology, vol. 88, no. 11, pp. 2783–2792, 2007.

L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.

H. S. Galal, Y. B. Mahdy, and M. A. Atiea, “Behavior-based features model for malware detection,” J. Comput. Virol. Hacking Tech., vol. 12, no. 2, pp. 59–67, 2016, doi: 10.1007/s11416-015-0244-0.

M. Staron, H. O. Hergés, L. Block, and M. Sjödin, “Comparing anomaly detection and classification algorithms: A case study in two domains,” in Int. Conf. Softw. Qual., 2023, pp. 121–136.

J. Henriques, F. Caldeira, T. Cruz, and P. Simões, “Combining k-means and XGBoost models for anomaly detection using log datasets,” Electronics, vol. 9, no. 7, p. 1164, 2020.

B. Apurva, “Anomaly detection in transactions.” Accessed: Feb. 17, 2025.

Published
2025-03-21
Abstract views: 287 times
Download PDF: 175 times
How to Cite
Ferdiansyah, F., Ependi, U., Tasmi, T., Haikal, M., & Mikko, M. (2025). Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning. Journal of Information Systems and Informatics, 7(1), 479-492. https://doi.org/10.51519/journalisi.v7i1.1047
Issue
Vol 7 No 1 (2025): March
Section
Articles

Copyright (c) 2025 Journal of Information Systems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

  1. I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
  2. I certify that all authors have approved the publication of this and there is no conflict of interest.
  3. I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
  4. I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
  6. I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
  7. If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
  8. I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)