Summary of Modsec-learn: Boosting Modsecurity with Machine Learning, by Christian Scano et al.
ModSec-Learn: Boosting ModSecurity with Machine Learning
by Christian Scano, Giuseppe Floris, Biagio Montaruli, Luca Demetrio, Andrea Valenza, Luca Compagna, Davide Ariu, Luca Piras, Davide Balzarotti, Battista Biggio
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ModSec-Learn model uses the Core Rule Set (CRS) as input features to tune the contribution of each rule to predictions, adapting the severity level to web applications. This approach achieves a better trade-off between detection and false positive rates compared to traditional heuristic-based methods. The model can also reduce the number of relevant CRS rules by up to 30% using sparse regularization. By leveraging machine learning, ModSec-Learn overcomes limitations in detecting web attacks based solely on heuristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach for protecting websites from malicious attacks is being proposed. Normally, protection systems use a set of predefined rules to detect and block harmful requests. However, these rules are not tailored to the specific website being protected. The new system uses machine learning to learn which rules are most important for each website. This results in better detection and fewer false alarms. The team also found that they could reduce the number of rules used by up to 30% without affecting performance. |
Keywords
» Artificial intelligence » Machine learning » Regularization
