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Web application firewall based on machine learning models.

Muhammed Ersin Durmuşkaya1, Selim Bayraklı2

  • 1Department of Computer Engineering, Istanbul Kultur University, Istanbul, Turkey.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary

Machine learning enhances web application security by detecting injection attacks. A decision tree algorithm proved most effective in a web application firewall (WAF), achieving high accuracy in real-time threat detection.

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Area of Science:

  • Cybersecurity
  • Machine Learning Applications
  • Web Application Security

Background:

  • Web applications are increasingly vital for sensitive data and financial transactions.
  • This reliance necessitates robust security measures against evolving cyber threats.
  • Injection vulnerabilities pose a significant risk to web application integrity.

Purpose of the Study:

  • To design and evaluate a machine learning-based web application firewall (WAF).
  • To protect web applications specifically against common injection vulnerabilities.
  • To compare the efficacy of various machine learning algorithms for threat detection.

Main Methods:

  • A hybrid dataset comprising CISC 2010, HTTPParams 2015, and real-time HTTP requests was utilized.
  • Five classification algorithms were assessed: K-nearest neighbors, logistic regression, naïve Bayes, support vector machine, and decision tree.
Keywords:
ClassificationInjectionMachine learningWAFWeb application firewallWeb security

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  • The algorithms were tested for their ability to detect Cross-Site Scripting (XSS), SQL Injection, OS Command Injection, and Local File Inclusion attacks.

    • Main Results:

    • The decision tree algorithm demonstrated superior performance across precision, accuracy, recall, F1-score, ROC, and AUC metrics.
    • Real-time testing of the WAF using the decision tree algorithm yielded an F1 score of 93.13% and an accuracy of 93.27%.
    • Confusion matrix analysis confirmed the high effectiveness of the machine learning-based WAF.

    Conclusions:

    • Machine learning-based WAFs provide effective protection against web application injection threats.
    • The decision tree algorithm is a highly suitable choice for developing such security systems.
    • Future research should explore broader attack coverage and diverse datasets for WAF enhancement.