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作者:

Wang, Xingzhen (Wang, Xingzhen.) | He, Jingsha (He, Jingsha.) | Zhu, Mingguang (Zhu, Mingguang.)

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EI Scopus

摘要:

With the introduction of blockchain technology and the development of virtual currencies, virtual currencies, which are based on cryptography to ensure the security and anonymity of transactions, have received a lot of attention. As a new payment method that is secure, decentralized and easy to transmit, virtual currencies have also attracted a large number of illegal users and illegal transactions. In order to identify gambling transaction behaviors and gambling-related addresses in the virtual currency market, this paper proposes a gambling transaction feature extraction method based on community detection and network embedding techniques, which obtains a network vector representation of this transaction network structure by discovering a high modularity and highly structured transaction network in gambling address transactions and performing node embedding and averaging calculations based on the node2vec algorithm to complete the extraction of transaction features of gambling addresses and solve the data imbalance problem of the huge gap between the number of historical transactions of different addresses. Finally, based on the feature dataset and several classical machine learning classification algorithms, a binary classification model is trained and evaluated to identify gambling transactions and addresses. Experiments show that all classification models achieve an accuracy rate of 0.72 or higher with high quality of transaction feature data, with the lightGBM model getting the best result of 0.84 accuracy rate, as well as 0.92 and 0.87 recall and F1 scores. © 2024 SPIE.

关键词:

Blockchain Network embeddings Extraction Virtual addresses Classification (of information) Feature extraction Population dynamics Data mining Crime

作者机构:

  • [ 1 ] [Wang, Xingzhen]Beijing University of Technology, Beijing, China
  • [ 2 ] [He, Jingsha]Beijing University of Technology, Beijing, China
  • [ 3 ] [Zhu, Mingguang]Beijing University of Technology, Beijing, China

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ISSN: 0277-786X

年份: 2024

卷: 13213

语种: 英文

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