计算机科学
智能合约
计算机安全
异常检测
一致性(知识库)
编码(集合论)
块(置换群论)
块链
脆弱性(计算)
推论
数据挖掘
人工智能
程序设计语言
几何学
数学
集合(抽象数据类型)
作者
Malaw Ndiaye,Thierno Ahmadou Diallo,Karim Konaté
标识
DOI:10.1016/j.bcra.2023.100148
摘要
Smart contracts are the building blocks of blockchain systems that enable automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analysing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behaviour of smart contracts, as a new feature. We design a learning and monitoring module to determine fraudulent smart contract behaviours.Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, the experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%), and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviours of smart contracts.
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