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language:
- en
pipeline_tag: text-classification
tags:
- web-application-firewall
- waf
- security
---
# 4thwall WAF Model
This model is a custom Web Application Firewall (WAF) classifier built by fine-tuning the `distilbert` (DistilBertForSequenceClassification) architecture. It is designed to identify and classify HTTP requests as either safe or potentially malicious (similarly to ModSecurity).
## Model Details
- **Model Type:** Text Classification (DistilBERT)
- **Task:** Identifying Malicious HTTP Requests (Web Application Firewall)
- **Use Case:** Can be used as a standalone classifier or inline ML-based proxy to analyze real-time HTTP traffic and reject high-risk requests (e.g., 403 Forbidden).
## Intended Uses & Limitations
- **Intended Use:** Inspecting HTTP paths, headers, and payloads for malicious intent (e.g., SQL Injection, XSS, etc.). Ideal for use within an ML pipeline integrating with services like Nginx or a customized inline WAF proxy.
- **Limitations:** The model acts as a learning proxy and can still result in False Positives or False Negatives. Continuous learning and manual feedback over time can help improve model confidence.
## Metrics
During evaluation, the model achieved the following metrics:
- **Accuracy:** 94.23%
- **Precision:** 92.50%
- **Recall:** 93.10%
- **F1 Score:** 92.80%
## How to Get Started with the Model
```python
from transformers import pipeline
# Load the WAF classifier
waf_classifier = pipeline("text-classification", model="your-username/my-waf-model")
# Example request payload
payload = "GET /index.php?id=1 UNION SELECT 1,2,3-- HTTP/1.1"
# Predict if malicious or benign
result = waf_classifier(payload)
print(result)
```
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