Machine Learning Model for Smart Contracts Security Analysis

被引：38
|
作者：
Momeni, Pouyan [1 ]
Wang, Yu [1 ]
Samavi, Reza [1 ]
机构：
[1] McMaster Univ, Hamilton, ON, Canada
来源：
2019 17TH INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST) | 2019年
基金：
加拿大自然科学与工程研究理事会;
关键词：
blockchain; smart contract; security vulnerability; machine learning; code analysis; software testing;
D O I：
10.1109/pst47121.2019.8949045
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：272 / 277
页数：6
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