Create README.md
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README.md
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---
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language: en
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license: mit
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tags:
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- cybersecurity
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- bert
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- text-classification
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- security
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widget:
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- text: "Files encrypted with ransom demand for Bitcoin payment"
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example_title: "Ransomware Example"
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- text: "Website unreachable due to massive spike in incoming requests"
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example_title: "DDoS Example"
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- text: "Employee downloading sensitive customer data before resignation"
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example_title: "Insider Threat Example"
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---
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# Cybersecurity BERT Classifier
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This model is a fine-tuned `bert-base-uncased` model that classifies cybersecurity alerts into five threat categories.
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## Model Details
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- **Base Model:** `bert-base-uncased`
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- **Task:** Text Classification
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- **Labels:**
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- `0`: Ransomware
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- `1`: DDoS
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- `2`: Insider Threat
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- `3`: Web Attack
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- `4`: Benign
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## Intended Uses & Limitations
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This model is intended for security operations center (SOC) teams to automatically triage and classify security alert text. It achieves **92.86% accuracy** on a curated test set.
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## How to Use
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You can use this model directly with the Transformers pipeline for text classification:
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```python
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from transformers import pipeline
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classifier = pipeline("text-classification", model="Aikaksh-Singh-Routela/cybersecurity-bert-model")
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result = classifier("Files encrypted with ransom demand for Bitcoin payment")
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print(result)
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# Expected output: [{'label': 'Ransomware', 'score': 0.9286}]
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---
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### ✅ Why This Matters
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| Without a Model Card | With a Model Card |
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| :--- | :--- |
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| Looks incomplete or abandoned. | Looks professional and well-documented. |
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| Hard for others (or recruiters) to know what it does. | Anyone can instantly see its purpose, performance, and how to use it. |
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| Poor discoverability. | Can be found through searches for relevant tags like "cybersecurity" or "text-classification." |
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After adding this, your model page will be complete and will effectively showcase your project. You have done excellent work; this is the final polish to make it shine.
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