Comparative Evaluation of Static Embedding Models for HTTP Request Anomaly Detection

arXiv:2609.26860v1 Announce Type: new Abstract: Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewalls (WAFs) rely on rule-based approaches that often exhibit high false positive rates and limited adaptability. Recent studies have explored machine learning techniques and word embedding models to improve anomaly detection in HTTP traffic.

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