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README.md

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----
-license: mit
----
+---
+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)
+```

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}