A feature selection-based method for SQL injection detection using machine learning algorithms

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dc.authorid0000-0001-5202-6315
dc.authorid0000-0003-1570-875X
dc.contributor.authorArasteh, Bahman
dc.contributor.authorSefati, Seyed Salar
dc.contributor.authorKarimi, Mohammadbagher
dc.contributor.authorInce, Ibrahim Furkan
dc.date.accessioned2026-01-31T15:08:45Z
dc.date.available2026-01-31T15:08:45Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
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.doi10.1177/18724981251385295
dc.identifier.endpage3956
dc.identifier.issn1872-4981
dc.identifier.issn1875-8843
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105025405763
dc.identifier.scopusqualityQ3
dc.identifier.startpage3939
dc.identifier.urihttps://doi.org./10.1177/18724981251385295
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10749
dc.identifier.volume19
dc.identifier.wosWOS:001632775200016
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofIntelligent Decision Technologies-Netherlands
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260128
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 machine learning algorithms
dc.typeArticle

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