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--- |
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model_name: Canstralian/CySec_Known_Exploit_Analyzer |
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tags: |
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- cybersecurity |
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- exploit-detection |
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- network-security |
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- machine-learning |
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license: mit |
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datasets: |
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- cysec-known-exploit-dataset |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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library_name: transformers |
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language: |
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- en |
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model_type: neural-network |
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base_model: |
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- replit/replit-code-v1_5-3b |
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--- |
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# CySec Known Exploit Analyzer |
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## Overview |
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- The CySec Known Exploit Analyzer is developed to: |
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- Detect and assess known cybersecurity exploits. |
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- Identify vulnerabilities and exploit attempts in network traffic. |
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- Provide real-time threat detection and analysis. |
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## Model Details |
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- **Type:** Neural Network |
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- **Input:** |
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- Network traffic logs |
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- Exploit payloads |
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- Related security information |
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- **Output:** |
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- Classification of known exploits |
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- Anomaly detection |
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- **Training Data:** |
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- Based on the [cysec-known-exploit-dataset](#datasets) |
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- Includes real-world exploit samples and traffic data. |
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- **Architecture:** |
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- Custom Neural Network with attention layers to identify exploit signatures in packet data. |
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- **Metrics:** |
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- Accuracy |
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- F1 Score |
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- Precision |
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- Recall |
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## Getting Started |
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**Installation** |
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1. Clone the repository: `git clone https://huggingface.co./Canstralian/CySec_Known_Exploit_Analyzer` |
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2. Navigate to the directory: `cd CySec_Known_Exploit_Analyzer` |
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3. Install the necessary dependencies: `pip install -r requirements.txt` |
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**Usage** |
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- To analyze a network traffic log: `python analyze_exploit.py --input [input-file]` |
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- **Example Command:** `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](LICENSE.md). |
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## Datasets |
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- The model is trained on the cysec-known-exploit-dataset, featuring exploit data from actual network traffic. |
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## Contributing |
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- Contributions are encouraged! Please refer to CONTRIBUTING.md for details. |
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## Contact |
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- For inquiries or feedback, please open an issue or contact [[email protected]](mailto:[email protected]). |
