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New ML model boosts open-source Web Application Firewall effectiveness

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ModSec-Learn, a machine learning model designed to enhance the effectiveness of ModSecurity, an open-source Web Application Firewall (WAF). ModSec-Learn utilizes the existing Core Rule Set (CRS) of ModSecurity as input features, allowing it to adapt the severity of rule contributions to specific web applications. This approach aims to improve detection rates and reduce false positives compared to traditional heuristic-based WAFs. The project also explores using sparse regularization to potentially discard over 30% of the CRS rules without compromising performance, with open-source code and datasets released for further development. AI

IMPACT This research could lead to more effective and adaptable web application security by leveraging machine learning to fine-tune WAF rules.

RANK_REASON The cluster describes a research paper detailing a new machine learning model for improving an existing open-source tool. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ML model boosts open-source Web Application Firewall effectiveness

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16 / 100
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The cluster describes a research paper detailing a new machine learning model for improving an existing open-source tool. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Christian Scano, Giuseppe Floris, Biagio Montaruli, Luca Demetrio, Andrea Valenza, Luca Compagna, Davide Ariu, Luca Piras, Davide Balzarotti, Battista Biggio ·

    ModSec-Learn: Boosting ModSecurity with Machine Learning

    arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicious requests by matching them against the Core Rule Set (CRS), identifying well-kn…

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