The basis of blockchain-related data, stored in distributed ledgers, are digitally signed transactions. Data can be stored on the blockchain ledger only after a digital signing process is performed by a user with a blockchain-based digital identity. However, this process is time-consuming and not user-friendly, which is one of the reasons blockchain technology is not fully accepted. In this paper, we propose a machine learning-based method, which introduces automated signing of blockchain transactions, while including also a personalized identification of anomalous transactions. In order to evaluate the proposed method, an experiment and analysis were performed on data from the Ethereum public main network. The analysis shows promising results and paves the road for a possible future integration of such a method in dedicated digital signing software for blockchain transactions.
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机译：存储在分布式LEDERS中的区块链相关数据的基础，是数字签名的交易。只有在具有基于区块链的数字身份的用户执行数字签名处理之后，数据可以仅存储在区块链分类帐上。然而，这个过程是耗时而不是用户友好的，这是区块链技术不完全被接受的原因之一。在本文中，我们提出了一种基于机器学习的方法，它介绍了区块链交易的自动签约，同时包括个性化的异常交易识别。为了评估所提出的方法，对来自Ethereum Public Main网络的数据进行实验和分析。分析显示了有希望的结果，并为在集区块交易中专用数字签名软件中的这种方法的未来集成，铺平了这条道路。
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