Batur Şahin, CananBatur Dinler, ÖzlemAbualigah, Laith2022-03-222022-03-222021Şahin, 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.https://doi.org/10.1007/s10489-021-02324-3https://hdl.handle.net/20.500.12899/774The 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.eninfo:eu-repo/semantics/closedAccessDeep learningSoftware vulnerabilityGenetic algorithmsSymbiotic learningDominance mechanismArticle10.1007/s10489-021-02324-35111827182872-s2.0-85103423186WOS:000635072300001Q2