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All Journal Journal of Information Systems and Informatics
Muhammad Haikal
Universitas Indo Global Mandiri
Computer Science & IT

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Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning Ferdiansyah Ferdiansyah; Usman Ependi; Tasmi Tasmi; Muhammad Haikal; Mikko Mikko
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1047

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.
Co-Authors Ferdiansyah Ferdiansyah Mikko Mikko Tasmi, Tasmi Usman Ependi