---
license: mit
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit
tags:
- cybersecurity
- mitre-attack
- honeypot
- log-analysis
- llama
- lora
- security
- threat-detection
language:
- en
datasets:
- custom
library_name: transformers
pipeline_tag: text-generation
---

# LLM-Enhanced Honeypot Log Analysis Model

## Model Description

This model is a fine-tuned version of Llama 3.1 8B Instruct, specialized for analyzing honeypot logs and generating MITRE ATT&CK framework annotations. It was developed as part of a research project at Queen's University Belfast investigating automated security log analysis using Large Language Models.

## Key Features

- **MITRE ATT&CK Annotation**: Automatically generates structured annotations for security events
- **Honeypot Log Analysis**: Specialized in analyzing Unix terminal logs from honeypot systems
- **LoRA Fine-tuning**: Uses Low-Rank Adaptation for efficient parameter updates
- **Research-Grade**: Developed for academic research in cybersecurity and AI

## Model Details

### Base Model
- **Base Model**: unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit
- **Model Size**: 8B parameters
- **Architecture**: Llama 3.1 with Instruct tuning
- **Quantization**: 4-bit quantization for efficiency

### Fine-tuning Details
- **Method**: LoRA (Low-Rank Adaptation)
- **LoRA Rank**: 32
- **LoRA Alpha**: 32
- **LoRA Dropout**: 0
- **Learning Rate**: 0.00012
- **Batch Size**: 2
- **Gradient Accumulation**: 4
- **Max Steps**: 100
- **Optimizer**: adamw_8bit

## Training Data

The model was trained on a curated dataset of honeypot logs with human-annotated MITRE ATT&CK framework labels. The training data includes:

- Unix terminal command logs from honeypot systems
- Structured annotations for 6 key MITRE ATT&CK fields
- Balanced representation of different attack tactics and techniques

## Usage

### Installation

```bash
pip install transformers torch unsloth
```

### Loading the Model

```python
from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="your-username/model-name",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)
```

### Inference

```python
# Enable inference mode
FastLanguageModel.for_inference(model)

# Format your input
prompt = '''Below is a Unix terminal command log from a honeypot system. Please analyze it and provide MITRE ATT&CK framework annotations.

Command: {command}
Timestamp: {timestamp}
Source IP: {source_ip}

Please provide:
1. Tactic
2. Technique
3. Sub-technique
4. Description'

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Evaluation

The model has been evaluated on multiple metrics:

- **Overall MITRE Accuracy**: Novel composite metric combining all 6 MITRE ATT&CK field accuracies
- **Confusion Matrix Analysis**: Visual analysis of tactics classification performance
- **Field-level Accuracy**: Individual accuracy for each MITRE ATT&CK field
- **Human Evaluation**: Expert validation of generated annotations

## Limitations

- Specialized for honeypot log analysis - may not generalize to other security contexts
- Requires structured input format for optimal performance
- Training data limited to specific honeypot configurations
- May exhibit biases present in training data

## Ethical Considerations

This model is designed for defensive cybersecurity research and should be used responsibly:

- Intended for legitimate security research and defense applications
- Should not be used for malicious purposes or unauthorized access
- Users should validate outputs before making security decisions
- Consider privacy implications when analyzing logs

## Citation

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

```bibtex
@misc{llm_honeypot_analysis_2025,
  title={LLM-Enhanced Honeypot Log Analysis System},
  author={[Student Name]},
  year={2025},
  institution={Queen's University Belfast},
  course={CSC4003 - Research Project},
  url={https://gitlab.eeecs.qub.ac.uk/[student-id]/CSC4003}
}
```

## License

This model is released under the MIT License. See the LICENSE file for details.

## Contact

For questions or issues:
- Repository: https://gitlab.eeecs.qub.ac.uk/40285272/CSC4006
- Institution: Queen's University Belfast
- Course: CSC4006 - Research Project

## Acknowledgments

- Built using the Unsloth library for efficient training
- Based on Meta's Llama 3.1 model
- Developed as part of cybersecurity research at Queen's University Belfast