A Feature Selection based Method for SQL Injection Detection Using Hybrid Machine Learning Algorithms

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dc.authorid0000-0003-0081-041X
dc.authorid0000-0001-5202-6315
dc.contributor.authorMohammadbagher Karimi
dc.contributor.authorBahman Arasteh Abbasabad
dc.contributor.authorSeyed Salar Sefati
dc.contributor.authorIbrahim Furkan Ince
dc.date.accessioned2026-01-27T12:22:47Z
dc.date.available2026-01-27T12:22:47Z
dc.date.issued8/12/2025
dc.departmentKapadokya Ɯniversitesi
dc.departmentDiğer
dc.description.abstractSQL injection (SQLi) is a serious security threat that allows attackers to access and manipulate databases through malicious input. Machine learning algorithms have shown strong potential for detecting SQL injection (SQLi) attacks. However, their performance depends heavily on the quality and relevance of the features used in training. Feature selection plays a key role in identifying the most effective, minimal set of features from the SQLi dataset. In this study, a hybrid SQLi detection method is proposed that combines feature selection with machine learning algorithms. A real-world dataset containing 13 features was first developed. Then, a hybrid Horse Herd Optimizer was developed and applied to select the most influential features before model training. Several machine learning classifiers were trained using the optimal feature set. The proposed method achieved high predictive performance, with 99.49% accuracy, 99.62% sensitivity, and 99.00% F1-score. These results were obtained using only about 45% of the original features. The reduction in feature size also improved the model's efficiency and training speed. The findings show that combining intelligent feature selection with machine learning significantly enhances SQLi detection. This approach is effective, scalable, and suitable for real-world security applications.
dc.identifier.doi10.1177/1872498125138529
dc.identifier.issn1872-4981
dc.identifier.scopus2-s2.0-105025405763
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://journals.sagepub.com/doi/abs/10.1177/18724981251385295?_gl=1*1lq18df*_up*MQ..*_ga*OTg2ODcwODA3LjE3Njk0MzUzMjE.*_ga_60R758KFDG*czE3Njk0MzUzMjAkbzEkZzAkdDE3Njk0MzUzMjAkajYwJGwwJGg5OTI0Nzg2NTE.
dc.identifier.urihttps://hdl.handle.net/20.500.12695/3948
dc.identifier.wosqualityN/A
dc.indekslendigikaynakScopus
dc.institutionauthorKarimi
dc.language.isoen
dc.publisherSAGE Publications- IOS PRESS
dc.relation.publicationcategoryGazete Makalesi - Uluslararası
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectCybersecurity
dc.subjectSQL injection
dc.subjectOptimal feature extraction
dc.subjectmachine learning algorithms
dc.subjectHorse Herd algorithm
dc.titleA Feature Selection based Method for SQL Injection Detection Using Hybrid Machine Learning Algorithms
dc.typeArticle

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