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Abstract

SQL Injection (SQLi) and Cross-Site Scripting (XSS) remain severe threats to web application security, particularly as attackers employ increasingly sophisticated obfuscation techniques to bypass conventional detection systems. This research constructs a machine learning framework using ensemble learning — specifically combining Random Forest and XGBoost — integrated with character-level n-gram feature extraction. The methodology involved rigorous data curation of a large-scale dataset, refining 156,636 raw samples into 151,783 unique entries to ensure high-quality training data. By extracting 10,000 character-level n-gram features, the model captures the intricate structural patterns of complex and obfuscated payloads. Experimental results show consistent and measurable performance: the proposed ensemble model achieved an overall accuracy of 99.67%. Stability was confirmed through a 5-fold cross-validation process, yielding a mean accuracy of 99.64% and a standard deviation of 0.0003. These findings are reinforced by ROC AUC scores of 1.0000 for XSS and 0.9999 for SQLi, indicating near-perfect discriminative capability. The combination of character-level representation and ensemble learning produces a precise and resilient solution for safeguarding modern web environments against dynamic and evolving cyber threats.

Keywords

Character-level N-gram Ensemble Learning SQL Injection Web Security XSS

How to Cite

Citation Format
Suharya, Y., & Anggara, M. B. (2026). Web Attack Detection for SQLi and XSS Using Ensemble Learning Based on Character-Level N-Gram Features. International Journal Software Engineering and Computer Science (IJSECS), 6(1), 391-403. https://doi.org/10.35870/ijsecs.v6i1.7193

References

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Author Biographies

Yaya Suharya, Universitas Bale Bandung

Department of Informatics Engineering, Universitas Bale Bandung, Bandung Regency, West Java Province, Indonesia

Mohammad Bayu Anggara, Universitas Bale Bandung

Department of Informatics Engineering, Universitas Bale Bandung, Bandung Regency, West Java Province, Indonesia

License

Copyright (c) 2026 Yaya Suharya, Mohammad Bayu Anggara

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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