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 has 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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Data Availability
The datasets analyzed during the current study are available in the GitHub repository [25], https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset.
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Acknowledgements
This work was supported in part by the National Key R &D Program of China (2021YFB2700500); in part by the National Natural Science Foundation of China (No. 61902348); and in part by the Key R &D Program of Zhejiang Province (No. 2021C01104).
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Huang, Z., Liu, Z., Chen, J. et al. Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods. Int J Multimed Info Retr 11, 539–551 (2022). https://doi.org/10.1007/s13735-022-00264-3
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DOI: https://doi.org/10.1007/s13735-022-00264-3