Detecting Smart Contract Vulnerabilities with Combined Binary and Multiclass Classification

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dc.contributor.authorMezina, Anzhelikacs
dc.contributor.authorOmetov, Aleksandrcs
dc.coverage.issue3cs
dc.coverage.volume7cs
dc.date.issued2023-07-07cs
dc.description.abstractThe development of Distributed Ledger Technology (DLT) is pushing toward automating decentralized data exchange processes. One of the key components of this evolutionary step is facilitating smart contracts that, in turn, come with several additional vulnerabilities. Despite the existing tools for analyzing smart contracts, keeping these systems running and preserving performance while maintaining a decent level of security in a constantly increasing number of contracts becomes challenging. Machine Learning (ML) methods could be utilized for analyzing and detecting vulnerabilities in DLTs. This work proposes a new ML-based two-phase approach for the detection and classification of vulnerabilities in smart contracts. Firstly, the system’s operation is set up to filter the valid contracts. Secondly, it focuses on detecting a vulnerability type, if any. In contrast to existing approaches in this field of research, our algorithm is more focused on vulnerable contracts, which allows to save time and computing resources in the production environment. According to the results, it is possible to detect vulnerability types with an accuracy of 0.9921, F1 score of 0.9902, precision of 0.9883, and recall of 0.9921 within reasonable execution time, which could be suitable for integrating existing DLTs.en
dc.formattextcs
dc.format.extent1-20cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCryptography. 2023, vol. 7, issue 3, p. 1-20.en
dc.identifier.doi10.3390/cryptography7030034cs
dc.identifier.issn2410-387Xcs
dc.identifier.other183983cs
dc.identifier.urihttp://hdl.handle.net/11012/213591
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofCryptographycs
dc.relation.urihttps://www.mdpi.com/2410-387X/7/3/34cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2410-387X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectmodelingen
dc.subjectclassificationen
dc.subjectvulnerability detectionen
dc.subjectdistributed systemsen
dc.titleDetecting Smart Contract Vulnerabilities with Combined Binary and Multiclass Classificationen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-183983en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:32en
sync.item.modts2025.01.17 15:21:23en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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