Anomaly Detection in Bitcoin Network: Using Distance-based and Tree-based Unsupervised Learning Methods

Anomaly Detection in Bitcoin Network: Using Distance-based and Tree-based Unsupervised Learning Methods

연구 분야: Artificial Intelligence

학회: BSCI '24: Proceedings of the 6th ACM International Symposium on Blockchain and Secure Critical Infrastructure

Anomaly detection in the financial system has been studied for decades. Anomalies refer to irregular items or events that are different from the majority. Therefore, illegal activities are anomalous by nature because it is opposed to the norm. Preventive actions like anomalous detection play an important role in avoiding incidents that damage people's property. In this paper, the Bitcoin network is the subject of the study. We consider the effectiveness of two unsupervised learning algorithms, Histogram-based Outlier Score (HBOS) and Isolation Forest, for detecting anomalous transactions and wallet addresses. Providing insights into the strengths and weaknesses of HBOS and Isolation Forest for anomaly detection. We also analyze which features are the most important for each algorithm in identifying anomalies. The result shows similar detection for both algorithms. While HBOS has higher wallet visualization score at 0.423, Isolation Forest yields better scores on transaction visualization, dual, and known-thieves evaluations with score of 0.713, 0.681, and 0.035, respectively.