과표본추출 기반의 블록체인 이상탐지 방법에 관한 연구The Study on Oversampling-Based Anomaly Detection in Blockchain Network
- Other Titles
- The Study on Oversampling-Based Anomaly Detection in Blockchain Network
- Authors
- 고자영; 배석주
- Issue Date
- Dec-2019
- Publisher
- 대한산업공학회
- Keywords
- Blockchain; Anomaly Detection; Network analysis; Data mining; Oversampling
- Citation
- 대한산업공학회지, v.45, no.6, pp.539 - 546
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 45
- Number
- 6
- Start Page
- 539
- End Page
- 546
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146623
- DOI
- 10.7232/JKIIE.2019.45.6.539
- ISSN
- 1225-0988
- 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 tra
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