Machine Learning Model for Smart Contracts Security Analysis

【Author】 Momeni, Pouyan; Wang, Yu; Samavi, Reza

【Source】2019 17TH INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST)

【Abstract】In this paper, we introduce a machine learning predictive model that detects patterns of security vulnerabilities in smart contracts. We adapted two static code analyzers to label more than 1000 smart contracts that were verified and used on the Ethereum platform. Our model predicted a number of major software vulnerabilities with the average accuracy of 95 percent. The model currently supports smart contracts developed in Solidity, however, the approach described in this paper can be applied to other languages and blockchain platforms.

【Keywords】blockchain; smart contract; security vulnerability; machine learning; code analysis; software testing

【标题】智能合约安全分析的机器学习模型

【摘要】在本文中，我们介绍了一个机器学习预测模型，它可以检测智能合约中的安全漏洞模式。我们采用了两个静态代码分析器来标记超过1000个在以太坊平台上验证和使用的智能合约。我们的模型预测了许多主要的软件漏洞，平均准确率为95%。该模型目前支持在solididity中开发的智能合约，然而，本文描述的方法可以应用于其他语言和区块链平台。

【关键词】区块链;智能合约;安全漏洞;机器学习;代码分析;软件测试

【发表时间】2019

【收录时间】2022-04-23

【文献类型】Proceedings Paper

【论文大主题】区块链监管

【论文小主题】智能合约监管

【翻译者】王佳鑫