Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

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dc.authorid0000-0002-2131-6368en_US
dc.contributor.authorBatur Şahin, Canan
dc.contributor.authorBatur Dinler, Özlem
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2022-03-22T13:03:08Z
dc.date.available2022-03-22T13:03:08Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractThe detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.en_US
dc.identifier.citationŞahin, C. B., Dinler, Ö. B., & Abualigah, L. (2021). Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features. Applied Intelligence, 51(11), 8271-8287.en_US
dc.identifier.doi10.1007/s10489-021-02324-3
dc.identifier.endpage8287en_US
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85103423186en_US
dc.identifier.startpage8271en_US
dc.identifier.urihttps://doi.org/10.1007/s10489-021-02324-3
dc.identifier.urihttps://hdl.handle.net/20.500.12899/774
dc.identifier.volume51en_US
dc.identifier.wosWOS:000635072300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorBatur Şahin, Canan
dc.language.isoenen_US
dc.relation.ispartofApplied Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectSoftware vulnerabilityen_US
dc.subjectGenetic algorithmsen_US
dc.subjectSymbiotic learningen_US
dc.subjectDominance mechanismen_US
dc.titlePrediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-featuresen_US
dc.typeArticleen_US

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