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과표본추출 기반의 블록체인 이상탐지 방법에 관한 연구

Title
과표본추출 기반의 블록체인 이상탐지 방법에 관한 연구
Other Titles
The Study on Oversampling-Based Anomaly Detection in Blockchain Network
Author
배석주
Keywords
Blockchain; Anomaly Detection; Network analysis; Data mining; Oversampling
Issue Date
2019-12
Publisher
대한산업공학회
Citation
대한산업공학회지, v. 45, no. 6, page. 539-546
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.
URI
http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09275219&language=ko_KRhttps://repository.hanyang.ac.kr/handle/20.500.11754/158145
ISSN
1225-0988; 2234-6457
DOI
10.7232/JKIIE.2019.45.6.539
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INDUSTRIAL ENGINEERING(산업공학과) > Articles
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