Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks

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dc.contributor.authorSahin, Canan Batur
dc.date.accessioned2026-06-19T06:41:15Z
dc.date.available2026-06-19T06:41:15Z
dc.date.issued2023
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractIn computer security, semantic learning is helpful in understanding vulnerability requirements, realizing source code semantics, and constructing vulnerability knowledge. Nevertheless, learning how to extract and select the most valuable features for software vulnerability detection remains difficult. In this paper, we first derive a subset of vulnerability knowledge representations from the Functional Connectivity (FC) of Graph Gated Sequence Neural Networks (GGNNs). The Gated Graph Sequence Neural Networks can be utilized to capture the long-term dependency to understand a high-level representation of potential vulnerabilities in order to detect vulnerabilities on a target project. Studying functional connectivity-based Graph Neural Networks ensures our deep understanding of the operation of sequence graph networks as highly complex interconnected systems. This ensures that the model focuses on vulnerability-related code, which makes it more appropriate for vulnerability mining tasks. Which constructs a composite semantic code property graph for code representation based on the causes of vulnerabilities. The experimental findings indicate that the suggested Model can select relevant discriminative features and achieve superior performance than benchmark methods.
dc.identifier.doi10.1007/s00500-022-07777-3
dc.identifier.endpage5719
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85145500304
dc.identifier.scopusqualityQ1
dc.identifier.startpage5703
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07777-3
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6138
dc.identifier.volume27
dc.identifier.wosWOS:000906642800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSahin, Canan Batur
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofSoft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectSoftware Vulnerability Detection
dc.subjectRepresentation Learning
dc.subjectFunctional Network Connectivity
dc.subjectGraph Neural Networks
dc.titleSemantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks
dc.typeArticle

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