A Machine Learning-based Intelligent Web Application Firewall For Real-time Protection Against Sql Injection And Xss Attacks

• Author(s):

  Darshan Karkar | Prof. Sweta Katariya

• Keywords:

  Web Application Firewall, SQL Injection, Cross-Site Scripting, Machine Learning, Anomaly Detection, Cybersecurity

• Abstract:

  The Rapid Growth Of Web Applications Has Led To An Increased Attack Surface For Cyberattacks Such As Structured Query Language (SQL) Injection, Cross-Site Scripting (XSS), And Other Application-layer Exploits. Traditional Web Application Firewalls (WAFs) That Rely Solely On Static, Signature-based Rules Struggle To Detect Obfuscated Payloads, Zero-day Attacks, And Novel Variants Of Existing Threats. This Paper Proposes An Intelligent Hybrid WAF Architecture That Combines Signature-based, Anomaly-based, And Machine Learning–based Detection To Provide Robust, Real-time Protection For Modern Web Applications. The System Monitors And Filters Hypertext Transfer Protocol (HTTP) Traffic Between Clients And The Web Application, Using A Multi-stage Detection Engine To Identify Malicious Requests And Apply Appropriate Mitigation Actions. The Proposed Model Leverages Public And Synthetic Web Attack Datasets For Training And Evaluation, With A Focus On SQLi And XSS Detection While Remaining Extensible To Other Emerging Threats. Expected Outcomes Include Improved Detection Accuracy, Reduced False Positives And False Negatives, Scalability In Cloud-native Environments, And A User-friendly Monitoring Dashboard That Supports Effective Security Operations.

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Other Details

• Paper id:

  IJSARTV12I3104700

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-12

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