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    姜人楷, 宋书玮, 罗夏朴, 陈厅, 罗瑞杰, 王炳森, 乔翱. DEALS——追踪代币转账信息不一致[J]. 计算机研究与发展, 2024, 61(2): 274-288. DOI: 10.7544/issn1000-1239.202330613
    引用本文: 姜人楷, 宋书玮, 罗夏朴, 陈厅, 罗瑞杰, 王炳森, 乔翱. DEALS——追踪代币转账信息不一致[J]. 计算机研究与发展, 2024, 61(2): 274-288. DOI: 10.7544/issn1000-1239.202330613
    Jiang Renkai, Song Shuwei, Luo Xiapu, Chen Ting, Luo Ruijie, Wang Bingsen, Qiao Ao. DEALS——Track Token Transfer Information Inconsistency[J]. Journal of Computer Research and Development, 2024, 61(2): 274-288. DOI: 10.7544/issn1000-1239.202330613
    Citation: Jiang Renkai, Song Shuwei, Luo Xiapu, Chen Ting, Luo Ruijie, Wang Bingsen, Qiao Ao. DEALS——Track Token Transfer Information Inconsistency[J]. Journal of Computer Research and Development, 2024, 61(2): 274-288. DOI: 10.7544/issn1000-1239.202330613

    DEALS——追踪代币转账信息不一致

    DEALS——Track Token Transfer Information Inconsistency

    • 摘要: 区块链使传统的交易所和借贷机构能够扩展到去中心化平台(decentralized platform,Depl),任何人都可以在没有中介机构帮助的情况下进行交易和借贷. 大多数Depl都是作为运行在以太坊上的智能合约实现的,并与另一种智能合约、加密货币(即代币)交互,以实现各种功能. 尽管Depl涉及价值超过350亿的美元,但人们对代币的实际转移是否如Depl预期的那样一致知之甚少. 代币的实际转移与Depl期望的不一致被称为行为不一致,这种不一致的出现将导致财产的损失和用户的质疑. 在这项工作中,我们迈出了调查Depl和代币之间的这种不一致的第一步. 我们提出通过监控Depl和代币的核心数据结构变化,将实际的代币转移行为与Depl内部记录指示的行为进行比较,自动检测不一致. 实验结果表明,在1012749笔交易中存在不一致行为,涉及2871对Depl和代币,与110个Depl和2544个代币相关. 实验结果的精准度为98.0%. 此外,还总结了导致不一致的10大原因,例如诈骗Depl、Depl与代币尺度不一致、锁币规则不明确等.

       

      Abstract: Blockchain enables traditional exchanges and lending houses to be extended to Depl (decentralized platforms), which allows anyone to access exchange and lending without the help of intermediaries. Most Depls are implemented as smart contracts running on Ethereum and interact with another smart contract, cryptocurrency (i.e. token), to achieve various functions. Although Depl involves more than 35 billion worth of tokens, little is known about whether the actual transfer of tokens is as consistent as Depl expects. The inconsistency between the actual transfer of tokens and what the decentralized platform expects is known as behavioral inconsistency, resulting in property damage and user confusion. In this work, we take the first step to investigate such inconsistency between Depl and tokens. We propose to automatically detect inconsistency by comparing the actual token transfer behavior with the behavior indicated by the internal records of Depl by monitoring the core data structure changes of Depl and token.The experimental results show that inconsistent behavior exists in 1012749 transactions with an accuracy of 98.0%, which involves 2871 pairs of Depl and tokens, and is related to 110 Depl and 2544 tokens.In addition, 10 main reasons behind the inconsistency are summarized, e.g., cheater Depl, inconsistent scale, unclear coin lock rules, etc.

       

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