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

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dc.authoridAbualigah, Laith/0000-0002-2203-4549
dc.authoridBATUR SAHIN, CANAN/0000-0002-2131-6368
dc.contributor.authorSahin, Canan Batur
dc.contributor.authorDinler, Ozlem Batur
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2024-12-24T19:24:36Z
dc.date.available2024-12-24T19:24:36Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
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.
dc.identifier.doi10.1007/s10489-021-02324-3
dc.identifier.endpage8287
dc.identifier.issn0924-669X
dc.identifier.issn1573-7497
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85103423186
dc.identifier.scopusqualityQ2
dc.identifier.startpage8271
dc.identifier.urihttps://doi.org/10.1007/s10489-021-02324-3
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6031
dc.identifier.volume51
dc.identifier.wosWOS:000635072300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofApplied Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectDeep learning
dc.subjectSoftware vulnerability
dc.subjectGenetic algorithms
dc.subjectSymbiotic learning
dc.subjectDominance mechanism
dc.titlePrediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features
dc.typeArticle

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