arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2301.01809cs.CR
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重要数字:使用大规模区块链数据预测欺诈性数字

Significant Digits: Using Large-Scale Blockchain Data to Predict Fraudulent Addresses

Jared Gridley, Oshani Seneviratne

摘要:

在过去的十年中,区块链系统和加密货币的普及程度呈爆炸式增长,随着用户群的不断增长,加密货币诈骗的数量也在激增。鉴于区块链网络的图形结构和这些网络上生成的大量数据,我们使用图挖掘技术来提取交易的基本信息,并应用本福德定律来提取地址交易的分布信息。然后,我们应用梯度提升树模型来预测欺诈地址。我们的研究结果表明,我们的方法可以检测到欺诈与合理的准确性和本福德定律的基础上产生的功能是最重要的功能。

英文摘要:

Blockchain systems and cryptocurrencies have exploded in popularity over the past decade, and with this growing user base, the number of cryptocurrency scams has also surged. Given the graphical structure of blockchain networks and the abundance of data generated on these networks, we use graph mining techniques to extract essential information on transactions and apply Benford's Law to extract distributional information on address transactions. We then apply a gradient-boosting tree model to predict fraudulent addresses. Our results show that our method can detect scams with reasonable accuracy and that the features generated based on Benford's Law are the most significant features.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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