Smart Contract Vulnerability Detection Technique: A Survey

TL;DR

This survey reviews smart contract vulnerabilities, detection techniques, and compares methods on real Ethereum contracts, highlighting challenges and future directions in enhancing security using advanced technologies.

Contribution

It provides a comprehensive classification of vulnerabilities and systematically compares various detection methods on real-world data, offering insights into current challenges and future research directions.

Findings

Compared detection methods on 300 Ethereum contracts.

Analyzed accuracy, F1-Score, detection time of techniques.

Discussed challenges and future trends in smart contract security.

Abstract

Smart contract, one of the most successful applications of blockchain, is taking the world by storm, playing an essential role in the blockchain ecosystem. However, frequent smart contract security incidents not only result in tremendous economic losses but also destroy the blockchain-based credit system. The security and reliability of smart contracts thus gain extensive attention from researchers worldwide. In this survey, we first summarize the common types and typical cases of smart contract vulnerabilities from three levels, i.e., Solidity code layer, EVM execution layer, and Block dependency layer. Further, we review the research progress of smart contract vulnerability detection and classify existing counterparts into five categories, i.e., formal verification, symbolic execution, fuzzing detection, intermediate representation, and deep learning. Empirically, we take 300 real-world smart contracts deployed on Ethereum as the test samples and compare the representative methods in terms of accuracy, F1-Score, and average detection time. Finally, we discuss the challenges in the field of smart contract vulnerability detection and combine with the deep learning technology to look forward to future research directions.