Secur-o-aMA

Overview

Secur-o-aMA is an advanced cybersecurity project that leverages Meta's LLaMA models to detect and mitigate cybersecurity threats. By fine-tuning LLaMA for specific tasks such as phishing detection, malware identification, and unauthorized access prevention, this project showcases the adaptability of large language models (LLMs) in the critical domain of cybersecurity.

This repository is designed to be both a practical tool for developers and an impressive demonstration of AI capabilities for hiring managers. It provides everything from raw data and model training scripts to comprehensive documentation on design decisions and performance optimizations.

Table of Contents

1. Project Goals
2. Features
3. Installation
4. Usage
5. File Structure
6. Dataset Details
7. Model Training
8. Evaluation & Results
9. Design Decisions
10. Performance Optimizations
11. Contributing
12. License
13. Contact

Project Goals

• Cybersecurity Enhancement: Develop a model that can detect and mitigate various cybersecurity threats using natural language processing.
• Demonstrate AI Flexibility: Showcase the versatility of Meta's LLaMA models in a non-traditional, security-focused context.
• Real-World Application: Provide a tool that has direct applicability in identifying and responding to cyber threats.
• Impress Hiring Managers: Exhibit advanced AI skills, understanding of cybersecurity, and ability to execute a full-stack project.

Features

• Fine-Tuned LLaMA Models: Specifically adapted for cybersecurity tasks.
• Comprehensive Documentation: Detailed insights into design decisions, model performance, and optimizations.
• User-Friendly Notebooks: Easy-to-follow Jupyter notebooks for data preprocessing, training, and evaluation.
• Free Resources: Built entirely using open-source tools and datasets, with no cost barriers to entry.
• Real-World Use Cases: Addresses practical issues such as phishing, malware detection, and unauthorized access.

In the README.md file, it's essential to include a Prerequisites section that outlines the necessary tools, libraries, and setup steps users need before they can run your project. Here’s what you should include for the Secur-o-aMA project:

Prerequisites

Before you begin, ensure you have met the following requirements:

1. Python 3.8+:

   • The project is developed using Python 3.8 or later. Make sure you have Python installed. You can download it from the official Python website.

2. Git:

   • You'll need Git installed to clone the repository. Download and install Git from here.

3. Kaggle API:

   • To download datasets from Kaggle, you'll need to set up the Kaggle API.
   • Steps to set up:
     1. Create a Kaggle account at Kaggle.com.
     2. Go to "My Account" and find the "API" section.
     3. Click "Create New API Token" to download a kaggle.json file.
     4. Place this file in the ~/.kaggle/ directory (create it if it doesn't exist).
     5. Ensure that the kaggle.json file has the correct permissions:

        chmod 600 ~/.kaggle/kaggle.json

4. Pip:

   • Ensure you have pip, the Python package manager, installed. It usually comes with Python, but you can update it using:

     python -m pip install --upgrade pip

5. API Access (For PhishTank dataset):

   • You’ll need an API key to access the PhishTank database. Sign up at PhishTank and include your API key in the scripts/download_initial_datasets.py script.

Installation

To run the project locally, you'll need to clone the repository and install the necessary dependencies.

# Clone the repository
git clone https://github.com/your-username/Secur-o-aMA.git

# Navigate to the project directory
cd Secur-o-aMA

# Create a virtual environment
python -m venv venv
course venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

You can also use Google Colab or GitHub Codespaces to run the project without setting up a local environment.

Usage

Download Initial Datasets

Run the download script to get the initial datasets

python scripts/download_initial_datasets.py

Data Preprocessing

Run the preprocessing script or the data_preprocessing.ipynb notebook to clean and prepare the datasets.

python scripts/preprocess_data.py

Model Training

Use the training notebook or script to fine-tune the LLaMA model on the provided cybersecurity datasets.

python scripts/train_model.py

Evaluation

Evaluate the model's performance using the evaluation notebook or script.

python scripts/evaluate_model.py

Deployment (Optional)

Deploy the trained model for real-time cybersecurity threat detection.

python scripts/deploy_model.py

File Structure

Secur-o-aMA/
│
├── data/
│   ├── raw/
│   ├── processed/
│   └── examples/
│
├── models/
│   ├── base/
│   ├── fine_tuned/
│   └── checkpoints/
│
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   └── evaluation.ipynb
│
├── scripts/
│   ├── preprocess_data.py
│   ├── train_model.py
│   ├── evaluate_model.py
│   └── deploy_model.py
│
├── results/
│   ├── metrics/
│   ├── logs/
│   └── performance_plots/
│
├── docs/
│   ├── design_decisions.md
│   ├── optimizations.md
│   └── performance_results.md
│
├── README.md
├── requirements.txt
└── LICENSE

Dataset Details

This project uses publicly available cybersecurity datasets. Examples include datasets for phishing detection, malware classification, and intrusion detection. More details on the datasets used, including sources and preprocessing steps, are provided in the data_preprocessing.ipynb notebook.

Model Training

The LLaMA model is fine-tuned using transfer learning on cybersecurity-specific datasets. The training process is documented in the model_training.ipynb notebook, which provides step-by-step instructions and explanations.

Evaluation & Results

The model's performance is evaluated on a separate test set, with metrics such as accuracy, precision, recall, and F1-score reported. These results are summarized in the evaluation.ipynb notebook and the performance_results.md document.

Design Decisions

All key design decisions, including the choice of datasets, model architecture, and training parameters, are detailed in the design_decisions.md document located in the docs/ directory. This transparency ensures that the project is understandable and reproducible.

Performance Optimizations

Various optimizations, such as hyperparameter tuning and model pruning, are explored to enhance the model's performance. These efforts are documented in optimizations.md in the docs/ directory.

Contributing

Contributions are welcome! Please read the CONTRIBUTING.md file for guidelines on how to contribute to this project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or suggestions, feel free to open an issue or contact me directly at [email protected].