Machine Learning Model for Smart Contracts Security Analysis

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 maj...

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Bibliographic Details
Main Authors: Momeni, Pouyan, Wang, Yu, Samavi, Reza
Format: Conference Proceeding
Language:English
Subjects:
blockchain
code analysis
machine learning
security vulnerability
smart contract
software testing
Citations: Items that cite this one
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Summary: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.
ISSN:2643-4202
DOI:10.1109/PST47121.2019.8949045