thisismon/network-vulnerability-classifier

Network Vulnerability Classification Model

Model Details

Model Description

• Developed by: thisismon
• Model type: Fine-tuned BERT (bert-base-uncased)
• Language: English
• License: MIT
• Finetuned from model: bert-base-uncased

Uses

Direct Use

This model is designed to classify network traffic logs as either normal or malicious. It can be used for:

• Network security monitoring
• Threat detection
• Traffic analysis

Out-of-Scope Use

• Not intended for production deployment without additional testing
• Not suitable for non-English network logs
• Not designed for real-time traffic analysis

Training Details

Training Data

• Binary classification dataset of network traffic logs
• Split: 80% training, 20% validation
• Data format: Text descriptions of network events

Training Procedure

• Framework: Hugging Face Transformers
• Epochs: 3
• Batch size: 16
• Warmup steps: 500
• Weight decay: 0.01
• Training time: 976.08 seconds

Training Results

• Accuracy: 94.65%
• Final Loss: 0.209
• Training metrics tracked with: Weights & Biases

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

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

# Example usage
text = "Unauthorized login attempt detected from IP 192.168.1.100"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
print("Malicious" if prediction == 1 else "Normal")

Environmental Impact

• Hardware Type: GPU
• Hours used: ~0.27 (16 minutes)
• Cloud Provider: Local
• Carbon Emitted: Minimal (local training)

Technical Specifications

Model Architecture

• Base model: BERT (bert-base-uncased)
• Added classification head for binary classification
• Input max length: 128 tokens
• Output: Binary classification (0: Normal, 1: Malicious)

Compute Infrastructure

• Python 3.8+
• PyTorch
• Transformers library
• GPU recommended for training