13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Author and article information

          Journal
          27 November 2022
          Article
          10.1007/s13735-022-00264-3
          2211.14779
          2c1f739b-9e81-4239-b075-d8f11599edf0

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          International Journal of Multimedia Information Retrieval (2022): 1-13
          cs.CR cs.LG q-fin.ST

          Security & Cryptology,Statistical finance,Artificial intelligence
          Security & Cryptology, Statistical finance, Artificial intelligence

          Comments

          Comment on this article