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Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain

2025-04-05Recensão Acesso aberto
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dc.contributor.authorCrisóstomo, João
dc.contributor.authorBação, Fernando
dc.contributor.authorLobo, Victor
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2025-01-07T21:29:06Z
dc.date.available2025-01-07T21:29:06Z
dc.date.issued2025-04-05
dc.descriptionCrisóstomo, J., Bação, F., & Lobo, V. (2025). Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain: A review. Expert Systems with Applications, 268, 1-16. Article 126353. https://doi.org/10.1016/j.eswa.2024.126353 --- %ABS1%
dc.description.abstractThis paper presents a comprehensive exploration of the intersection between machine learning and smart contract vulnerabilities on the Ethereum blockchain. Introduced by Vitalik Buterin in 2015, Ethereum stands as a prominent blockchain network, necessitating innovative approaches to secure smart contracts against vulnerabilities and potential attacks. This research follows PRISMA guidelines, posing three fundamental questions and conducting a meticulous literature review. The study categorises machine learning applications into seven distinct groups, analysing their taxonomy, feature types, and engineering methods. The findings indicate a dynamic landscape characterised by a noticeable trend towards increased complexity. This complexity is evident not only in the integration of machine learning frameworks that combine different architectures of deep learning models, such as Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), or Recurrent Neural Networks (RNN), but also in the incorporation of various types of data related to smart contracts (SCs). The discussion dissects the advantages, limitations, and future directions in securing smart contracts using machine learning. The paper concludes by emphasising the evolving role of machine learning in strengthening the Ethereum blockchain, fostering trust, and enhancing security in decentralised systems.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent1412584
dc.identifier.doi10.1016/j.eswa.2024.126353
dc.identifier.issn0957-4174
dc.identifier.otherPURE: 105961552
dc.identifier.otherPURE UUID: 78023d23-ec1a-421a-9d56-46b0dd71a533
dc.identifier.othercrossref: 10.1016/j.eswa.2024.126353
dc.identifier.otherScopus: 85213940554
dc.identifier.otherWOS: 001402760000001
dc.identifier.urihttp://hdl.handle.net/10362/177128
dc.identifier.urlhttps://www.scopus.com/pages/publications/85213940554
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001402760000001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectReview
dc.subjectSmart Contract
dc.subjectVulnerabilities
dc.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectGeneral Engineering
dc.subjectComputer Science Applications
dc.subjectArtificial Intelligence
dc.titleMachine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchainen
dc.title.subtitleA reviewen
dc.typereview
degois.publication.firstPage1
degois.publication.lastPage16
degois.publication.titleExpert Systems with Applications
degois.publication.volume268
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)