Comparison of Fusion and Hybrid Machine Learning Models on Anomaly Detection for Network Web Security

The proliferation of web applications has made HTTP a prime vector for cyber-attacks including SQL injection (SQLi), cross-site scripting (XSS), command injection, and parameter tampering.

Traditional Web Application Firewalls (WAFs) rely on manually maintained signatures that struggle against obfuscated payloads and novel variants.

While deep learning and transformer-based models achieve high detection rates, they incur significant computational cost and deployment complexity.

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