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thisismon/
network-vulnerability-classifier-balanced

safetensors

bert

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Network Traffic Vulnerability Classifier

Model Description

This model is fine-tuned BERT for binary classification of network traffic patterns as either normal or malicious.

Performance Metrics

Latest evaluation results:

Accuracy: 80.00%
Precision: 71.43%
Recall: 100.00%
F1 Score: 83.33%

Intended Use

Primary use: Network traffic monitoring and threat detection
Suitable for: Real-time traffic analysis and batch processing
Target users: Security analysts and automated monitoring systems

Limitations

High recall (100%) but lower precision (71.43%) indicates:
- Model tends to over-classify traffic as malicious
- May generate false positives
- Better suited for initial screening than final decision-making
Performance characteristics:
- More sensitive to malicious patterns
- May need human verification for borderline cases
- Best used with confidence thresholds

Training Details

Base model: bert-base-uncased
Training data: Balanced dataset of normal and malicious traffic patterns
Regularization:
- Hidden dropout: 0.4
- Attention dropout: 0.4
- Classifier dropout: 0.4
Training parameters:
- Learning rate: 5e-6
- Batch size: 32
- Weight decay: 0.4
- Early stopping patience: 2

Bias and Fairness

Model shows bias toward malicious classification
Implemented class weights [1.0, 2.0] to handle imbalanced classes
May need adjustment based on specific deployment context

Recommendations

Implementation:
- Use confidence threshold (recommended: 0.85)
- Implement human review for high-risk predictions
- Monitor false positive rates in production
Best practices:
- Regular retraining with new data
- Validation with domain-specific traffic patterns
- Integration with other security tools

Example Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-username/network-vulnerability-classifier")
tokenizer = AutoTokenizer.from_pretrained("your-username/network-vulnerability-classifier")

# Make prediction
def predict(text, confidence_threshold=0.85):
    inputs = tokenizer(text, return_tensors="pt", truncation=True)
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
    prediction = outputs.logits.argmax(-1).item()
    confidence = probs[prediction].item()
    
    if confidence < confidence_threshold:
        return "Uncertain", confidence
    return "Normal" if prediction == 0 else "Malicious", confidence

Updates and Maintenance

Last updated: [Current Date]
Regular updates planned for:
- Performance improvements
- New traffic patterns
- Security vulnerabilities

Citation

If you use this model in your research, please cite:

@misc{network-vulnerability-classifier,
  author = {Your Name},
  title = {Network Traffic Vulnerability Classifier},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub}
}

thisismon/network-vulnerability-classifier-balanced