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Analysis of Bitcoin Transactions to Detect Illegal Transactions Using Convolutional Neural Networks

机译：比特币交易分析使用卷积神经网络检测非法交易

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摘要

The article is devoted to virtual currencies, which is a fast growing and popular market. It was found that for virtual currencies, in particular, for the cryptocurrency Bitcoin, there is a problem of uncontrolled money laundering. This is facilitated by pseudo-anonymization and the presence of illegal exchangers. In this paper, to solve this problem, the method of combining layers in convolutional neural networks is used, which is manifested in the stack layering.In CNN networks, convolutional and erecting layers are usually stacked in a stack, one above the other. The paper proposes a model of Bitcoin transaction analysis to identify anomalies related to money laundering. As such a model, it is proposed to take a combined method, which consists of the method of random forests, enhanced by information from the graph convolutional network, ie, embedded vertices. As a result of the model, we obtained indicators that indicate the presence of possible shadow transactions in the amount of 2-3% of the total market.
机译：这篇文章致力于虚拟货币，这是一个快速增长和流行的市场。有人发现，对于虚拟货币，特别是对于加密货币比特币，有一个不受控制的洗钱问题。这是通过伪匿名化和非法交易所的存在而促进的。在本文中，为了解决这个问题，使用组合卷积神经网络中的层的方法，该方法在堆叠层中表现出。在CNN网络中，卷积和架设层通常堆叠在堆叠中，一个在另一个上方。本文提出了比特币交易分析模型，以识别与洗钱有关的异常。作为这样的模型，建议采用组合方法，该方法由随机林的方法组成，通过图表卷积网络的信息增强，即嵌入式顶点。由于该模型，我们获得了指标，表示可能的影子交易的存在，占总市场的2-3％。

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