ReDetect : Reentrancy Vulnerability Detection in Smart Contracts with High Accuracy

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2021 17th International Conference on Mobility, Sensing and Networking
Subtitle of host publicationMSN 2021
PublisherIEEE
Pages412-419
ISBN (Electronic)9781665406680
ISBN (Print)978-1-6654-0669-7
Publication statusPublished - Dec 2021

Publication series

NameProceedings - International Conference on Mobility, Sensing and Networking, MSN

Conference

Title17th International Conference on Mobility, Sensing and Networking (MSN 2021)
LocationVirtual
PlaceUnited Kingdom
CityExeter
Period13 - 15 December 2021

Abstract

Smart contracts are a landmark achievement of blockchain technology 2.0 and are widely adopted in various applications. However, smart contracts are not always secure and there are various vulnerabilities. The reentrancy vulnerability is one of most serious vulnerabilities, and it has caused huge economic losses. Although many methods have been proposed to detect reentrancy vulnerabilities, they all have high false positives. To deal with this problem, we propose a symbolic execution-based detection tool for reentrancy vulnerabilities of smart contracts at the EVM bytecode level. By analyzing a large number of real-world smart contracts, we conclude main patterns of false positives and design five effective path filters to eliminate false positives. We evaluate its performance on real-world datasets in comparison with the state-of-the-art works, and the results show that our tool is more effective in the detection of reentrancy vulnerabilities.

Research Area(s)

  • false positives, path filters, Reentrancy vulnerability, smart contracts, symbolic execution

Citation Format(s)

ReDetect : Reentrancy Vulnerability Detection in Smart Contracts with High Accuracy. / Yu, Rutao; Shu, Jiangang; Yan, Dekai et al.

Proceedings - 2021 17th International Conference on Mobility, Sensing and Networking: MSN 2021. IEEE, 2021. p. 412-419 (Proceedings - International Conference on Mobility, Sensing and Networking, MSN).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review