以太坊应用程序的事件日志提取方法

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-10-18 DOI:10.1016/j.future.2024.107566
Andrea Morichetta, Yuri Paoloni, Barbara Re
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引用次数: 0

摘要

在基于区块链的去中心化应用中采用智能合约可以实现可靠的认证审计。通过这些审计,可以从区块链中提取有价值的信息,这些信息可用于重建应用程序的执行并促进高级分析。在这种情况下,最常用的技术之一是流程挖掘,它利用事件日志来跟踪和准确表示应用程序的流程执行情况。然而,从区块链中提取执行数据面临着巨大挑战,目前开发的方法也存在一些局限性。大多数方法都是为特定用例量身定制的,需要在智能合约开发过程中定义分析技术。其他技术是事后应用的,依赖于通常缺乏标准化格式的区块链事件。这种标准化的缺失需要复杂的处理步骤才能将日志与执行的操作关联起来,而且这种方法并不适用于区块链上的所有智能合约,从而进一步限制了其应用范围。最后,现有技术都无法从嵌入智能合约内部交易的事件日志中提取信息。为了解决这些局限性,我们提出了 EveLog,这是一种应用无关的方法,可以应用于任何 EVM 兼容的应用,而无需预定义的限制。它的主要目标是从智能合约中提取信息,捕捉公共和内部交易,并将结果整理成结构化的 XES 事件日志。EveLog 方法包括五个关键步骤:(i) 从智能合约交易中提取数据,(ii) 对原始数据进行解码,(iii) 选择排序标准,(iv) 构建轨迹,(v) 生成 XES 事件日志。EveLog 已在客户端-服务器应用程序中实施,并在现有解决方案上进行了测试,特别是 CryptoKitties 应用程序,这是一款基于以太坊区块链的区块链游戏。这项研究使用了 12996 个区块,包括来自以太坊主网的 8000 多笔真实交易。
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Event log extraction methodology for Ethereum applications
The adoption of smart contracts in decentralized blockchain-based applications enables reliable and certified audits. These audits allow the extraction of valuable information from blockchains, which can be used to reconstruct the execution of the application and facilitate advanced analyses. One of the most commonly used techniques in this context is process mining, which leverages event logs to trace and accurately represent the process execution of applications. However, extracting execution data from blockchains poses significant challenges, and the current methodologies developed have some limitations. Most approaches are tailored to specific use cases, requiring that analysis techniques are defined during the smart contract’s development. Other techniques are applied a posteriori, relying on blockchain events that often lack a standardized format. This absence of standardization requires complex processing steps to correlate logs with the executed actions and such approaches are not universally applicable to all smart contracts on the blockchain, further limiting their scope. Lastly, none of the existing techniques can extract information from event logs embedded in internal transactions of smart contracts.
To address these limitations, we propose EveLog an application-agnostic methodology that can be applied to any EVM-compatible application without predefined constraints. Its primary goal is to extract information from smart contracts, capturing both public and internal transactions, and organizing the results into a structured XES event log. The EveLog methodology consists of five key steps: (i) extraction of data from smart contract transactions, (ii) decoding raw data, (iii) selection of sorting criteria, (iv) construction of traces, and (v) generation of the XES event log. EveLog has been implemented in a client–server application and tested on existing solutions, specifically the CryptoKitties application, a blockchain-based game on the Ethereum blockchain. The study was conducted using 12,996 blocks, including over 8000 real transactions from the Ethereum mainnet.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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