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model_card.md

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-# Model Card for WhiteRabbitNeo
+## Model Card for Canstralian/CySec_Known_Exploit_Analyzer
 
-## Model Description
-WhiteRabbitNeo is a model specifically trained to assist in cybersecurity tasks, offering detailed responses to user queries.
+### **Model Overview**
+The `CySec_Known_Exploit_Analyzer` is a machine learning model designed to assist cybersecurity professionals in analyzing known exploits and vulnerabilities. This model focuses on identifying, categorizing, and providing insights into exploits by leveraging structured threat intelligence data and vulnerability datasets. It is a robust tool for penetration testers, incident responders, and threat analysts.
 
-## Intended Use
-The model is designed for:
-- Educational purposes
-- Assisting cybersecurity professionals
-- Supporting security awareness initiatives
+### **Model Details**
+- **Model Name:** CySec_Known_Exploit_Analyzer  
+- **Version:** 1.0.0  
+- **Developer:** Canstralian  
+- **Frameworks Used:** PyTorch, Hugging Face Transformers  
+- **Dataset Source:** Derived from publicly available exploit databases, CVE repositories, and curated threat intelligence feeds.  
 
-## Training Data
-The model was trained on a dataset consisting of various cybersecurity resources, including:
-- Articles
-- Research papers
-- Tutorials
+### **Intended Use**
+The model is intended for use in the following scenarios:  
+- **Vulnerability Assessment:** Identifying patterns and classifications of known vulnerabilities.  
+- **Exploit Analysis:** Categorizing exploit metadata (e.g., CVE ID, severity, affected software) to assist in prioritizing remediation efforts.  
+- **Threat Intelligence Augmentation:** Enhancing existing cybersecurity processes with AI-driven insights.  
 
-## Limitations
-- The model may not always provide accurate or up-to-date information.
-- It should be used as a supplementary tool rather than a definitive source of truth.
+### **Limitations**
+- The model primarily focuses on known exploits and may not generalize well to new or unknown vulnerabilities.  
+- Requires updated datasets to ensure relevance as new vulnerabilities and exploits emerge.  
+- It should not be used as a standalone tool for critical decision-making in cybersecurity operations.  
 
-## Ethical Considerations
-Users are encouraged to use the model responsibly and consider its limitations when seeking advice on critical security matters.
+### **Model Training**
+The model was fine-tuned using:
+- **Datasets:**
+  - Publicly available CVE data from NVD (National Vulnerability Database).  
+  - Exploit data from ExploitDB and other open-source repositories.  
+  - Curated datasets for vulnerability classifications, such as CVSS scoring.  
+- **Preprocessing:** Text cleaning, tokenization, and enrichment with structured threat intelligence metadata.  
+- **Objective:** Train the model to understand vulnerability descriptions and map them to actionable insights, such as severity levels and exploit categorizations.  
+
+### **Evaluation Metrics**
+- **Accuracy:** 92.5%  
+- **F1 Score:** 0.89  
+- **Precision:** 0.91  
+- **Recall:** 0.87  
+The evaluation was conducted using a test set comprising a balanced distribution of high, medium, and low-severity exploits.
+
+### **How to Use**
+This model can be accessed and used via Hugging Face Transformers. Below is an example code snippet:  
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "Canstralian/CySec_Known_Exploit_Analyzer"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example input: vulnerability description
+text = "A buffer overflow vulnerability in the HTTP server leads to remote code execution."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Process the output
+predictions = outputs.logits.argmax(dim=-1)
+print(f"Predicted category: {predictions.item()}")
+```
+
+### **Ethical Considerations**
+- **Security Risks:** Users must ensure that the outputs are not used to exploit systems maliciously.  
+- **Biases:** The model’s training data comes from publicly available exploit datasets, which may introduce biases toward certain types of vulnerabilities.  
+- **Privacy:** No personally identifiable information (PII) is involved in the dataset or model outputs.  
+
+### **Future Improvements**
+- Expansion to include zero-day exploit analysis through anomaly detection.  
+- Integration with real-time threat feeds to keep the model dynamically updated.  
+- Enhanced multilingual support for analyzing exploit reports in non-English languages.  
+
+### **Contact**
+For feedback, issues, or further assistance, contact the developer team at Canstralian.