SHIBUYA WAF: Open-Source Web Application Firewall with Machine Learning and eBPF Security
By
germainluperto
Plain bagel done well. Pleasantly substantive.
Summary
SHIBUYA WAF is a next-generation open-source web application firewall that combines traditional rule-based security with machine learning and eBPF kernel-level blocking. It features a dual ML engine with explainable AI using SHAP to detect and classify attacks across 10 categories including SQLi, XSS, RCE, and others. The system includes 615+ OWASP CRS rules, GraphQL/API protection, WASM plugins, and a built-in attack lab for testing.
Key quotes
· 4 pulledDual ML Engine with SHAP Explainability: Two independent ML models run in parallel: an IsolationForest (via ONNX runtime) detects anomalies, and a Random Forest classifies attacks across 10 classes
SHAP-like explainability shows exactly which top-5 features triggered the alert. Human-in-the-loop feedback loop. A/B model testing. Data drift detection
Next-gen open-source WAF. ML detection with explainability. eBPF kernel blocking. 615+ CRS rules. GraphQL/API protection. WASM plugins. Built-in attack lab
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