A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

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dc.authorid0000-0002-2131-6368en_US
dc.contributor.authorBatur Şahin, Canan
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2021-05-26T08:30:32Z
dc.date.available2021-05-26T08:30:32Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionality, and as a result, several vulnerabilities are often overlooked. This paper introduces a novel approach for effectively and reliably finding vulnerabilities in open-source software programs. In this paper, we are motivated to examine the potential of the clonal selection theory. A novel deep learning-based vulnerability detection model is proposed to define features using the clustering theory of the clonal selection algorithm. To our knowledge, this is the first time we have used deep-learned long-lived team-hacker features to process memories of sequential features and mapping from the entire history of previous inputs to target vectors in theory. With an immune-based feature selection model, the proposed approach aimed to improve static analyses' detection abilities. A real-world SA dataset is used based on three open-source PHP applications. Comparisons are conducted based on using a classification model for all features to measure the proposed feature selection methods' classification improvement. The results demonstrated that the proposed method got significant enhancements, which occurred in the classification accuracy also in the true positive rate.en_US
dc.identifier.citationŞahin, C. B., & Abualigah, L. (2021). A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection. Neural Computing and Applications, 1-19.en_US
dc.identifier.doi10.1007/s00521-021-06047-x
dc.identifier.endpage19en_US
dc.identifier.scopus2-s2.0-85105376457en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06047-x
dc.identifier.urihttps://hdl.handle.net/20.500.12899/147
dc.identifier.wosWOS:000645963700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBatur Şahin, Canan
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectFeature selectionen_US
dc.subjectImmune systemsen_US
dc.subjectSoftware vulnerability predictionen_US
dc.titleA novel deep learning-based feature selection model for improving the static analysis of vulnerability detectionen_US
dc.typeArticleen_US

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