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  - replit/replit-code-v1_5-3b
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  ---
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- # Canstralian/CySec_Known_Exploit_Analyzer
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  ## Overview
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- The **CySec Known Exploit Analyzer** is a model designed to detect and analyze known cybersecurity exploits. This model was built to assist in identifying vulnerabilities and exploit attempts in network traffic by leveraging machine learning algorithms. It is designed for real-time detection and analysis of potential threats.
 
 
 
 
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  ## Model Details
 
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  - **Type:** Neural Network
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- - **Input:** Network traffic logs, exploit payloads, or relevant security data
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- - **Output:** Classification of known exploits, anomaly detection
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- - **Training Data:** Trained on the **cysec-known-exploit-dataset**, which includes real-world exploit samples and traffic data.
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- - **Architecture:** Custom Neural Network with attention layers for detecting exploit signatures in packet data.
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- - **Metrics:** The model was evaluated using accuracy, F1 score, precision, and recall to measure its performance.
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Getting Started
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- ### Installation
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- To clone the repository and install the necessary dependencies:
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-
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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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- ```
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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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-
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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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  ## 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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- <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 any questions or feedback, feel free to open an issue or reach out to [[email protected]].
 
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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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+
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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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+
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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).