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README.md

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----
-license: mit
----
+## README.md (with Metadata)
+```yaml
+---
+model_name: "Canstralian/CySec_Known_Exploit_Analyzer"
+tags:
+  - cybersecurity
+  - exploit-detection
+  - network-security
+  - machine-learning
+license: mit
+datasets:
+  - "cysec-known-exploit-dataset"
+metrics:
+  - accuracy
+  - f1
+  - precision
+  - recall
+library_name: transformers
+language:
+  - en
+model_type: neural-network
+---
+```
+
+# Canstralian/CySec_Known_Exploit_Analyzer
+
+## Overview
+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.
+
+## Model Details
+- **Type:** Neural Network
+- **Input:** Network traffic logs, exploit payloads, or relevant security data
+- **Output:** Classification of known exploits, anomaly detection
+- **Training Data:** Trained on the **cysec-known-exploit-dataset**, which includes real-world exploit samples and traffic data.
+- **Architecture:** Custom Neural Network with attention layers for detecting exploit signatures in packet data.
+- **Metrics:** The model was evaluated using accuracy, F1 score, precision, and recall to measure its performance.
+
+## Getting Started
+
+### Installation
+To clone the repository and install necessary dependencies:
+```bash
+git clone https://huggingface.co/Canstralian/CySec_Known_Exploit_Analyzer
+cd CySec_Known_Exploit_Analyzer
+pip install -r requirements.txt
+```
+
+### Usage
+To analyze a network traffic log:
+```bash
+python analyze_exploit.py --input [input-file]
+```
+
+### Example
+```bash
+# Example command to analyze a sample log
+python analyze_exploit.py --input data/sample_log.csv
+```
+
+### Model Inference
+- **Input:** Network traffic logs in CSV format
+- **Output:** Classification of potential exploits with confidence scores
+
+## License
+This project is licensed under the MIT License. See the LICENSE.md file for more details.
+
+## Datasets
+The model was trained using the **cysec-known-exploit-dataset**, which consists of exploit data collected from real-world network traffic.
+
+## Contributing
+We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
+
+## Contact
+For any questions or feedback, feel free to open an issue or reach out to [Your Email].
+
+---
+
+### **Metadata Fields Explained:**
+- **`model_name`:** The name of the model.
+- **`tags`:** Keywords for discoverability.
+- **`license`:** Chosen license (MIT).
+- **`datasets`:** Dataset used for training.
+- **`metrics`:** Metrics for model evaluation.
+- **`library_name`:** Library used to build the model.
+- **`language`:** Language of the documentation.
+- **`model_type`:** Type of model.
+
+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!
+
+## Commit Options
+- Commit directly to the **main** branch.
+- Open as a pull request to the **main** branch.
+```