Text Classification
Transformers
Safetensors
English
distilbert
cybersecurity
xss
security
web
payload-detection
web-security
Instructions to use kd7979148/XSS_Payload_Detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd7979148/XSS_Payload_Detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kd7979148/XSS_Payload_Detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kd7979148/XSS_Payload_Detector") model = AutoModelForSequenceClassification.from_pretrained("kd7979148/XSS_Payload_Detector") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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The request will be reflected by the test page and analyzed by the monitoring service.
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## Model Files
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- config.json
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The request will be reflected by the test page and analyzed by the monitoring service.
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### Components
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- `inference_bert_url.py`
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- Standalone CLI tool for testing XSS detection.
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- `moniter.py`
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- Log monitoring service that reads web server logs and analyzes incoming requests.
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- `test_server.py`
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- Flask-based demonstration server for testing reflected XSS scenarios.
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- `templates/`
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- HTML templates used by the Flask demonstration server.
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- `static/`
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- Static assets (images, CSS, etc.) used by the Flask demonstration server.
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## Model Files
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- config.json
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