基于图的深度学习模型，用于检测比特币交易期间非法交易

在比特币交易期间确定和防止非法交易是私人金融市场的挑战性问题。

Cryptocurrency has become increasingly used these days. It is a digital currency, i.e. the encrypted data strings which only exist electronically. They are not decentralized; in other words, they are not issued by the government. Cryptography algorithms are used to create cryptocurrency and they are validated through mining1. If the miners are successful, they will receive cryptocurrency as a reward2,3,4. These electronic currencies are stored in a structure and it is called blockchain. Blockchain gives information with node details in a distributed environment. The information cannot be edited or ruined. Transactions occur in blocks of data, and each block has a pointer to the previous block. The arrival of Bitcoin has triggered the popularity of cryptocurrency5. However, this recurring use of cryptocurrency has captured the attention of many scammers. Cryptocurrency scams occur through many means; ransomware attack, financial crimes, Ponzi schemes, etc. However, cryptocurrency is a small network that provides anonymity to its users. Scammers take advantage of this and use this facility to camouflage themselves.

如今，加密货币已经越来越多地使用。这是一种数字货币，即仅在电子上存在的加密数据字符串。他们不是分散的；换句话说，它们不是由政府发行的。加密算法用于创建加密货币，并通过Mining1进行验证。如果矿工成功，他们将获得加密货币作为奖励2,3,4。这些电子货币存储在结构中，称为区块链。区块链在分布式环境中提供了带有节点详细信息的信息。信息不能编辑或破坏。交易发生在数据块中，每个块都有指向上一个块的指针。比特币的到来触发了加密货币的普及5。但是，这种反复使用的加密货币吸引了许多骗子的注意。加密货币骗局通过多种方式发生。勒索软件攻击，金融犯罪，庞氏骗局等。但是，加密货币是一个为用户提供匿名的小网络。骗子利用这一点，并利用此设施伪装自己。

Bitcoin

比特币

Bitcoin is one of the first cryptocurrency. It works by using blockchain and is more valuable than others. The bitcoin blockchain can be viewed as a shared ledger in which every transaction is recorded and cannot be changed. Each block has a unique hash pointer which points to the previous block. These points are the links between each block. Nodes in the network would validate the new transaction and is added to the ledger or the blockchain which is then published to the rest of the nodes. When mining is done to create new bitcoin, the miner gets the unique hash value of the new coin using algorithms. In recent days, the Bitcoin’s pseudonymity is used for illegal activities such as goods transport and banking transactions6.

比特币是第一个加密货币之一。它通过使用区块链而起作用，并且比其他区块更有价值。比特币区块链可以看作是共享分类帐，其中每次交易被记录并且不能更改。每个块都有一个唯一的哈希指针，指向上一个块。这些点是每个块之间的链接。网络中的节点将验证新事务，并将其添加到分类帐或区块链中，然后将其发布到其余节点。采矿以创建新的比特币时，矿工使用算法获得了新硬币的唯一哈希值。最近几天，比特币的化名用于非法活动，例如商品运输和银行交易6。

Anti money laundering in cryptocurrency

在加密货币中洗钱

Weaker sections of society struggle to be included in the financial process of the country. Financial inclusion basically means providing access to these groups of society to participate in the country’s financial development by providing them with financial services and products at a reasonable cost. But these marginal groups are restricted access to these systems, which is an accidental repercussion of Anti Money Laundering (AML) regulations. Unfortunately, these regulations cannot be avoided to maintain the protection of our economy. AML also results in relatively higher costs on products. AML regulations are mandatory and must be followed because many illegal transactions are identified and thus avoided.

社会较弱的部分难以将其纳入该国的财务过程中。金融包容基本上意味着通过为这些社会群体提供访问权，以合理的成本为他们提供金融服务和产品，从而参与该国的财务发展。但是这些边际群体受到限制对这些系统的访问，这是对反洗钱（AML）法规的意外影响。不幸的是，这些法规无法避免维持对我们经济的保护。 AML还导致产品成本相对较高。 AML法规是强制性的，必须遵循，因为确定了许多非法交易并因此避免。

Literature review

文献综述

The financial marketing studies say that Bitcoin purchase increases in 2024. While the volume of transactions increases, fraudulent activities also increased7. Dhanya8 collected the Bitcoin data from 2013 to 2018 and proposed rollover techniques for better prediction of accuracy in the time series model. She considered the Bitcoin date from The bitcoin data from 2013 to 2018. Also recent machine learning methods overcome the time series mode. In this paper, we consider the data till 2024. The proposed security protocol for Bitcoin is one of the important aspects. Recently, Badertscher et al.9 proposed a security proof for the Bitcoin protocol. They proposed a secure protocol for global clock and hash functions. As an application and case study, Kumar et al.10 proposed a model for blockchain-based land registration. In an unpublished Arxiv paper, Weber et al.6 proposed different machine learning models to predict illicit transactions. They have found precision, recall, F1 score, and micro-average. For illicit transactions with GCN, their precision value is 0812, Recall score is 0.512 and R1 score is 0.628. Our experimental results are shown in the section 4.1, which improved their results. Chen et al.11 gave a detailed survey on different machine learning methods to detect suspicious transactions on Bitcoin11. In this paper, they have an analysis of some of the important aspects such as risk scoring, link analysis and behavioral modeling. Klaus Grobys et al.12 proposed a statistical distribution model for stolen Bitcoins. Features extractions are one of the important in the study of Bitcoin transactions. Vlahavas et al.13 used a clustering algorithm for feature extractions. Pranav Nerurka14 proposed a graph attention network (GAT2) with accuracy of to identify illegal transactions.

金融营销研究表明，比特币购买在2024年增加。虽然交易量增加，但欺诈活动也增加了7。 Dhanya8从2013年到2018年收集了比特币数据，并提出了拟议的翻转技术，以更好地预测时间序列模型的准确性。她考虑了比特币的日期，从2013年到2018年的比特币数据。最近的机器学习方法也克服了时间序列模式。在本文中，我们考虑到2024年的数据。比特币的拟议安全协议是重要方面之一。最近，Badertscher等人9提出了比特币协议的安全证明。他们为全局时钟和哈希功能提出了一个安全的协议。作为一项应用和案例研究，Kumar等人10提出了一个基于区块链的土地注册的模型。 Weber等人在未发表的Arxiv论文中。6提出了不同的机器学习模型来预测非法交易。他们发现了精度，召回，F1分数和微平均水平。对于与GCN的非法交易，其精度值为0812，召回得分为0.512，R1得分为0.628。我们的实验结果显示在第4.1节中，这改善了其结果。 Chen等人11对不同的机器学习方法进行了详细的调查，以检测比特币上的可疑交易。在本文中，他们对一些重要方面进行了分析，例如风险评分，链接分析和行为建模。 Klaus Grobys等人112提出了一个被盗比特币的统计分布模型。提取物是比特币交易研究中的重要之一。 Vlahavas等人13使用聚类算法进行特征提取。 Pranav Nerurka14提出了一个图形注意网络（GAT2），其准确性以识别非法交易。

Motivations and key contributions

动机和关键贡献

Identifying and preventing the illegal transactions during bitcoin transactions are challenging problems in the private financial market. The actor called a “miner” (person/machine) will participate and validate all the Bitcoin transactions. The miner is considered the most important person for the creation of new cryptocurrencies and their validations. The minor’s decision is based on his mathematical model used for the transactions. Even a small error in the prediction model leads to a huge financial loss for the transacting people. For example, during the 10-year period 2011–2021, nearly 1.7 million Bitcoin units were stolen due to criminal activity, with losses exceeding 700 million12. So,

在比特币交易期间确定和防止非法交易是私人金融市场的挑战性问题。演员称为“矿工”（人/机器）将参与并验证所有比特币交易。矿工被认为是创建新的加密货币及其验证的最重要的人。未成年人的决定基于他用于交易的数学模型。即使是预测模型中的一个小错误也会导致交易人员造成巨大的财务损失。例如，在2011 - 2021年的10年期间，由于犯罪活动，将近170万个比特币单位被盗，损失超过7亿12。所以，