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README.md
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- replit/replit-code-v1_5-3b
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#
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## Overview
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## Model Details
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- **Type:** Neural Network
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- **Input:**
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## Getting Started
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To clone the repository and install the necessary dependencies:
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```bash
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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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python analyze_exploit.py --input [input-file]
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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
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## Datasets
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The model
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## Contributing
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<iframe src="https://github.com/sponsors/canstralian/card" title="Sponsor canstralian" height="225" width="600" style="border: 0;"></iframe>
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## Contact
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For
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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:distortedprojection@gmail.com).
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