---
language: en
license: mit
tags:
- security
- log-analysis
- anomaly-detection
- codebert
- lora
library_name: transformers
pipeline_tag: text-classification
---

# Security Event Detector - CodeBERT with LoRA

## Model Description

This model detects security-relevant events in system logs using CodeBERT fine-tuned with LoRA (Low-Rank Adaptation).

**Task**: Binary classification (Normal vs Security Event)

**Base Model**: microsoft/codebert-base

**Fine-tuning Method**: LoRA (98% parameter reduction)

## Training Data

Trained on synthetic and real security logs including:
- Authentication failures
- Exploit attempts
- Buffer overflows
- Network attacks
- Privilege escalation attempts

## Performance

- **Accuracy**: ~95%
- **F1 Score**: ~0.94
- **Inference Speed**: ~50ms per log (GPU)

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
tokenizer = AutoTokenizer.from_pretrained("Swapnanil09/security-event-detector")
model = AutoModelForSequenceClassification.from_pretrained("Swapnanil09/security-event-detector")

# Analyze log
log = "Failed password for root from 192.168.1.100 port 22 ssh2"
inputs = tokenizer(log, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1)
    is_security = prediction.item() == 1

print(f"Security Event: {is_security}")
```

## Model Details

- **Parameters**: ~125M (only ~2M trainable with LoRA)
- **Input**: System log text (max 128 tokens)
- **Output**: Binary classification (0=Normal, 1=Security)
- **Confidence Scores**: Softmax probabilities included

## Limitations

- Trained primarily on English logs
- May not detect novel/zero-day attacks
- Performance depends on log format similarity to training data

## Citation

```bibtex
@misc{security-event-detector,
  author = {Your Name},
  title = {Security Event Detector with CodeBERT and LoRA},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Swapnanil09/security-event-detector}}
}
```

## License

MIT License