Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods

• URL: http://arxiv.org/abs/2211.14779v1
• Date: Sun, 27 Nov 2022 10:07:13 GMT
• Title: Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
• Authors: Zhengjie Huang, Zhenguang Liu, Jianhai Chen, Qinming He, Shuang Wu, Lei Zhu, Meng Wang
• Abstract summary: We propose a tool termed ETHGamDet to discover gambling behaviors and identify the contracts and addresses involved in gambling. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. We present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications.
• Score: 46.17004007514548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling have transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights.

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