A Concise Review of Anomaly Detection Approaches for Block Chain Transactions

Jibrin Baba Isah; Asabe Sandra Ahmadu; Ajayi Gbolahan Babatunde; Abubakar; Bello; Corresponding Author

doi:10.56201/ijcsmt.vol.11.no12.2025.pg210.219

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A Concise Review of Anomaly Detection Approaches for Block Chain Transactions

Jibrin Baba Isah, Asabe Sandra Ahmadu, Ajayi Gbolahan Babatunde, Abubakar, Bello, Corresponding Author

Abstract

Blockchain systems have revolutionized decentralized data management yet continue to be vulnerable to irregular and fraudulent transaction activities. Identifying such anomalies is difficult because of the large complexity, class imbalance, and dynamic characteristics of blockchain data. This paper provides a concise empirical review of anomaly detection methodologies utilized in blockchain transactions, encompassing statistical techniques, machine learning, ensemble learning, Explainable Artificial Intelligence , deep learning, and graph-based strategies. The review consolidates methodological trends, strengths, and limitations identified in recent research. Findings indicate an increasing transition towards ensemble-based learning such as Random Forest, and hybrid methodologies owing to their resilience and versatility. The study concludes that ensemble-based learning models, including RF, outperform single classifiers such as SVM in terms of robustness and detection accuracy.

Keywords

Block chainAnomaly detectionRandom ForestSupport Vector MachineEnsemble learningFraud detection.

References

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A Concise Review of Anomaly Detection Approaches for Block Chain Transactions

Abstract

Keywords

References

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