AI-Driven Web Attack Detection Framework for Enhanced Payload Classification
Analysis
This paper presents WAMM, an AI-driven framework for web attack detection, addressing the limitations of rule-based WAFs. It focuses on dataset refinement and model evaluation, using a multi-phase enhancement pipeline to improve the accuracy of attack detection. The study highlights the effectiveness of curated training pipelines and efficient machine learning models for real-time web attack detection, offering a more resilient approach compared to traditional methods.
Key Takeaways
- •WAMM is an AI-driven framework for web attack detection.
- •It uses a multi-phase enhancement pipeline for dataset refinement.
- •XGBoost achieved high accuracy with fast inference.
- •WAMM outperforms rule-based systems in detecting attacks.
Reference
“XGBoost reaches 99.59% accuracy with microsecond-level inference using an augmented and LLM-filtered dataset.”