---
license: mit
tags:
- log-analysis
- anomaly-detection
- bert
- cybersecurity
- multiclass-classification
language:
- en
datasets:
- custom-log-dataset
metrics:
- f1
- accuracy
pipeline_tag: text-classification
---

# DANN-BERT-Log-Anomaly-Detection - Log Anomaly Detection

This model is part of the **Log Anomaly Detection System** that classifies system logs into 7 anomaly categories.

## Model Description

DANN-BERT-Log-Anomaly-Detection is a Domain-Adversarial Neural Network BERT model fine-tuned for multi-class log anomaly detection. It can classify logs from 16+ different sources (Apache, SSH, Hadoop, etc.) into 7 categories:

1. **Normal** (0): Benign operations
2. **Security Anomaly** (1): Authentication failures, unauthorized access
3. **System Failure** (2): Crashes, kernel panics  
4. **Performance Issue** (3): Timeouts, slow responses
5. **Network Anomaly** (4): Connection errors, packet loss
6. **Config Error** (5): Misconfigurations, invalid settings
7. **Hardware Issue** (6): Disk failures, memory errors


## Performance Metrics

- **F1-Score (Macro)**: 0.903
- **Accuracy**: 0.921
- **Model Type**: Domain-Adversarial Neural Network BERT
- **Classes**: 7 (normal, security_anomaly, system_failure, performance_issue, network_anomaly, config_error, hardware_issue)


## Usage

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

# Load the model
model = torch.load('model.pt')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

# Example usage
log_text = "Apr 15 12:34:56 server sshd[1234]: Failed password for admin"
inputs = tokenizer(log_text, return_tensors='pt', max_length=128, truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)
```

## Training Data

- **Sources**: 16 log types (Apache, SSH, Hadoop, HDFS, Linux, Windows, etc.)
- **Size**: ~32,000 labeled logs
- **Classes**: 7 anomaly categories
- **Features**: BERT embeddings + template features + statistical features

## Citation

```bibtex
@misc{log-anomaly-detection-2024,
  title={Log Anomaly Detection System},
  author={Krishna Sharma},
  year={2024},
  url={https://github.com/krishnasharma4415/log-anomaly-detection}
}
```

## License

MIT License - see LICENSE file for details.