WAPP: Safe Learning of Positive Security WAF Policies from Live Traffic

2026-09-06

AI Summary

本文提出WAPP框架，通过信任过滤、规则合成等方法从实时流量中安全地学习正向安全策略，以解决WAF对未知或变种攻击的防护不足问题。

Abstract

Web Application Firewalls (WAFs) mainly rely on signatures to detect known attacks, which can leave gaps against modified or previously unseen payloads. Positive security provides a complementary approach by learning legitimate traffic and blocking inputs that fall outside the learned profile. However, learning directly from live traffic can be unsafe when malicious requests contaminate the training data. This paper presents the Whitelisting Autonomous Policy Producer (WAPP), a framework that combines trust filtering, deterministic rule synthesis, confidence scoring, and validation before enforcement. WAPP is evaluated on three controlled applications using a live Coraza and OWASP Core Rule Set (CRS) stack. Results show that, on the tested DVWA username field, unfiltered learning becomes Degraded at 0.2\% poisoned traffic and Broken at 0.5%, while the evaluated free text field can admit malicious inputs even without poisoning. On the frozen poisoning dataset, the ablation configuration with all seven candidate signals improves the measured poisoning resilience from 53% to 90%, compared with 62% for the Kruegel--Vigna baseline. The deterministic synthesizer provides attack blocking comparable to the tested language model without model inference cost. WAPP blocks confirmed CRS bypasses on constrained fields, while free text inputs remain a precision challenge that requires character level operator control.

Problem

• Web Application Firewalls
• Positive Security
• Live Traffic Learning
• Malicious Requests

Innovation

• Whitelisting Autonomous Policy Producer
• trust filtering
• deterministic rule synthesis
• confidence scoring