과표본추출 기반의 블록체인 이상탐지 방법에 관한 연구

Abstract

Despite the characteristics of reliable blockchain, there are an increasing trend of anomalies in its network. Recent crime reports show that bitcoins can be used in illegal transactions such as drug trafficking, money laundering and frauds. Thus, it is crucial to detect illegal transactions earlier to secure credibility of blockchain network. We extracted features from both each users and their transactions after building a database. In particular, transaction data are of a network structure, so features are extracted using the network analysis. Owing to unbalance property of the transaction data, the borderline SMOTE is used as the oversampling method. Finally, the analysis and comparison are performed using support vector machine (SVM), random forest (RF), XGBoost, and logistic regression to evaluate their performances. We apply the proposed method to the real data set of bitcoin transaction data, and find that XGBoost shows the best performance in detecting anomal transactions. The proposed oversampling-based methods show a potential in detecting anomal transactions earlier.