Enhanced SQL injection detection using chi-square feature selection and machine learning classifiers Article Swipe
Emanuel Casmiry
,
Neema Mduma
,
Ramadhani Sinde
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2025.1686479
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2025.1686479
In the face of increasing cyberattacks, Structured Query Language (SQL) injection remains one of the most common and damaging types of web threats, accounting for over 20% of global cyberattack costs. However, due to its dynamic and variable nature, the current detection methods often suffer from high false positive rates and lower accuracy. This study proposes an enhanced SQL injection detection using Chi-square feature selection (FS) and machine learning models. A combined dataset was assembled by merging a custom dataset with the SQLiV3.csv file from the Kaggle repository. A Jensen–Shannon Divergence (JSD) analysis revealed moderate domain variation (overall JSD = 0.5775), with class-wise divergence of 0.1340 for SQLi and 0.5320 for benign queries. Term Frequency-Inverse Document Frequency (TF-IDF) was used to convert SQL queries into feature vectors, followed by the Chi-square feature selection to retain the most statistically significant features. Five classifiers, namely multinomial Naïve Bayes, support vector machine, logistic regression, decision tree, and K-nearest neighbor, were tested before and after feature selection. The results reveal that Chi-square feature selection improves classification performance across all models by reducing noise and eliminating redundant features. Notably, Decision Tree and K-Nearest Neighbors (KNN) models, which initially performed poorly, showed substantial improvements after feature selection. The Decision Tree improved from being the second-worst performer before feature selection to the best classifier afterward, achieving the highest accuracy of 99.73%, precision of 99.72%, recall of 99.70%, F1-score of 99.71%, a false positive rate (FPR) of 0.25%, and a misclassification rate of 0.27%. These findings highlight the crucial role of feature selection in high-dimensional data environments. Future research will investigate how feature selection impacts deep learning architectures, adaptive feature selection, incremental learning approaches, robustness against adversarial attacks, and evaluate model transferability across production web environments to ensure real-time detection reliability, establishing feature selection as a vital step in developing reliable SQL injection detection systems.
Related Topics
Concepts
Feature selection
Computer science
Artificial intelligence
Decision tree
Machine learning
Support vector machine
Feature (linguistics)
Classifier (UML)
Logistic model tree
Pattern recognition (psychology)
Decision tree learning
Data mining
Precision and recall
SQL
Random forest
Statistical classification
Naive Bayes classifier
F1 score
ID3 algorithm
SQL injection
Word error rate
Incremental decision tree
False positive rate
Selection (genetic algorithm)
Feature vector
Training set
Feature extraction
Information gain ratio
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.3389/fdata.2025.1686479
- https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1686479/pdf
- OA Status
- gold
- References
- 33
- OpenAlex ID
- https://openalex.org/W7106024027
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106024027Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fdata.2025.1686479Digital Object Identifier
- Title
-
Enhanced SQL injection detection using chi-square feature selection and machine learning classifiersWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-19Full publication date if available
- Authors
-
Emanuel Casmiry, Neema Mduma, Ramadhani SindeList of authors in order
- Landing page
-
https://doi.org/10.3389/fdata.2025.1686479Publisher landing page
- PDF URL
-
https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1686479/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://public-pages-files-2025.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1686479/pdfDirect OA link when available
- Concepts
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Feature selection, Computer science, Artificial intelligence, Decision tree, Machine learning, Support vector machine, Feature (linguistics), Classifier (UML), Logistic model tree, Pattern recognition (psychology), Decision tree learning, Data mining, Precision and recall, SQL, Random forest, Statistical classification, Naive Bayes classifier, F1 score, ID3 algorithm, SQL injection, Word error rate, Incremental decision tree, False positive rate, Selection (genetic algorithm), Feature vector, Training set, Feature extraction, Information gain ratioTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
33Number of works referenced by this work
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| publication_date | 2025-11-19 |
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