Blockchain illegal gambling identification based on community detection and network embedding

Xingzhen Wang; Jingsha He; Mingguang Zhu

doi:10.1117/12.3035419

19 July 2024 Blockchain illegal gambling identification based on community detection and network embedding

Xingzhen Wang, Jingsha He, Mingguang Zhu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321314 (2024) https://doi.org/10.1117/12.3035419
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

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) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingzhen Wang, Jingsha He, and Mingguang Zhu "Blockchain illegal gambling identification based on community detection and network embedding", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321314 (19 July 2024); https://doi.org/10.1117/12.3035419

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KEYWORDS

Data modeling

Detection and tracking algorithms

Machine learning

Feature extraction

Blockchain

Education and training

Random forests

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