Canstralian
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README.md
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## README.md (with Metadata)
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```yaml
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---
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model_name: "Canstralian/CySec_Known_Exploit_Analyzer"
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tags:
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model_type: neural-network
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```
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# Canstralian/CySec_Known_Exploit_Analyzer
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git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
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cd CySec_Known_Exploit_Analyzer
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pip install -r requirements.txt
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```
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To analyze a network traffic log:
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python analyze_exploit.py --input [input-file]
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```
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# Example command to analyze a sample log
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python analyze_exploit.py --input data/sample_log.csv
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```
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## License
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This project is licensed under the MIT License. See the LICENSE.md file for more details.
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The model was trained using the **cysec-known-exploit-dataset**, which consists of exploit data collected from real-world network traffic.
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We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
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For any questions or feedback, feel free to open an issue or reach out to [Your Email].
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- **`model_name`:** The name of the model.
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- **`tags`:** Keywords for discoverability.
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- **`license`:** Chosen license (MIT).
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- **`datasets`:** Dataset used for training.
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- **`metrics`:** Metrics for model evaluation.
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- **`library_name`:** Library used to build the model.
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- **`language`:** Language of the documentation.
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- **`model_type`:** Type of model.
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This structure will enhance discoverability and usability for users on platforms like Hugging Face or GitHub. If you need any more adjustments or additions, let me know!
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## Commit Options
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- Commit directly to the **main** branch.
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- Open as a pull request to the **main** branch.
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```
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---
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model_name: "Canstralian/CySec_Known_Exploit_Analyzer"
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tags:
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- en
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model_type: neural-network
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---
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# Canstralian/CySec_Known_Exploit_Analyzer
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git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
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cd CySec_Known_Exploit_Analyzer
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pip install -r requirements.txt
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Usage
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To analyze a network traffic log:
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python analyze_exploit.py --input [input-file]
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Example
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# Example command to analyze a sample log
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python analyze_exploit.py --input data/sample_log.csv
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Model Inference
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• Input: Network traffic logs in CSV format
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• Output: Classification of potential exploits with confidence scores
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License
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This project is licensed under the MIT License. See the LICENSE.md file for more details.
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Datasets
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The model was trained using the cysec-known-exploit-dataset, which consists of exploit data collected from real-world network traffic.
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Contributing
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We welcome contributions! Please see CONTRIBUTING.md for guidelines.
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Contact
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For any questions or feedback, feel free to open an issue or reach out to [[email protected]].
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