Intrusion-Detection-Systems-using-ML

📜 Project Overview

This project focuses on developing an Intrusion Detection System (IDS) using machine learning techniques. The IDS aims to analyze datasets for intrusion patterns, preprocess data efficiently, and prepare the foundation for applying machine learning models.

🚀 Features

• Dataset Preprocessing: Handles large datasets efficiently using Polars.
• Data Exploration: Detailed exploration and analysis of datasets.
• Data Manipulation: Handles data cleaning, feature engineering, and processing.
• Performance Optimization: Optimized processing of large datasets compared to traditional libraries like Pandas.

📂 Repository Structure

Intrusion-Detection-Systems-using-ML/
├── input/                   # Raw dataset files from Car Hacking Dataset
│   ├── attack_free.txt
│   ├── dos_dataset.csv
│   ├── fuzzy_dataset.csv
├── output/                  # Processed datasets for analysis
│   ├── attack_free_df.csv
│   ├── dos_df.csv
│   ├── fuzzy_df.csv
├── notebooks/                   # Jupyter notebooks for testing and analysis
│   ├── eda.ipynb                # Exploratory data analysis using Pandas
│   ├── preprocess_data.ipynb    # Data manipulation and feature engineering
│   ├── fix_data_issue.ipynb     # Solving misplaced dlc-flag column issue 
│   ├── visualize_data.ipynb     # Data visualization with graphics
│   ├── utils.ipynb              # Some helper functions
├── src/                         # Final Python scripts for processing data
│   ├── get_data_pandas.py       # Dataset loading with Pandas
│   ├── load_data.py             # Optimized dataset loading with Polars
│   ├── preprocess_data.py       # Data manipulation functions
│   ├── train_model.py           # Data modelling
│   ├── utils.py                 # Helper Functions
├── README.md                # Project documentation (you're reading this)

📊 Datasets

The raw datasets are taken from the Car Hacking Dataset, which contains records for intrusion detection, such as:

• attack_free.txt: Attack-free dataset.
• dos_dataset.csv: Denial of Service (DoS) dataset.
• fuzzy_dataset.csv: Fuzzy intrusion dataset.

Processed datasets are saved in the output folder as:

• attack_free_df.csv
• dos_df.csv
• fuzzy_df.csv

🛠️ Setup Instructions

1. Clone the repository:

   git clone https://github.com/yourusername/Intrusion-Detection-Systems-using-ML.git
   cd Intrusion-Detection-Systems-using-ML

2. Install dependencies:
   Create and activate a virtual environment, then install requirements:

   python -m venv venv
   source venv/bin/activate   # For Linux/Mac
   venv\Scripts\activate      # For Windows
   pip install -r requirements.txt

3. Run preprocessing scripts: Use the src scripts to generate processed datasets:

   python src/load_data.py
   
   

📝 Usage

• Load data: Process large datasets with optimized Polars methods in src/load_data.py.
• Exploration: Use notebooks/eda.ipynb to explore datasets.
• Manipulation: Modify datasets using notebooks/preprocess_data.ipynb or src/preprocess_data.py.
• Visualization: Visualize datasets using notebooks/visualize_data.ipynb

🤝 Contributions

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

📜 License

• This project is licensed under the MIT License.

🙌 Acknowledgments

• Car Hacking Dataset: The source of the raw datasets.
• Polars Library: For efficient data manipulation.
• Open-source community for inspiration and tools.