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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:
@@ -20,7 +18,6 @@ language:
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  - en
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  model_type: neural-network
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  ---
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- ```
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  # Canstralian/CySec_Known_Exploit_Analyzer
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@@ -43,51 +40,35 @@ To clone the repository and install necessary dependencies:
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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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- ### Usage
 
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  To analyze a network traffic log:
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- ```bash
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  python analyze_exploit.py --input [input-file]
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- ```
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- ### Example
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- ```bash
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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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- ### 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](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 [Your Email].
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- ---
 
 
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- ### **Metadata Fields Explained:**
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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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-
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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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-
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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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+
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  To analyze a network traffic log:
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+
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  python analyze_exploit.py --input [input-file]
 
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+ Example
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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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+ Model Inference
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+
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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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+
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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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+
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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]].