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

Authors

  • 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
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DOI:

https://doi.org/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.

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Published

2025-03-21

Issue

Vol. 7 No. 1 (2025): March

Section

Articles

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How to Cite

[1]
F. Ferdiansyah, U. Ependi, T. Tasmi, M. Haikal, and M. Mikko, “Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning”, journalisi, vol. 7, no. 1, pp. 479–492, Mar. 2025, doi: 10.51519/journalisi.v7i1.1047.

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