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https://github.com/b3ta-blocker/raspberry-pi-based-intrusion-detection-system

Raspberry Pi-based Intrusion Detection System is a lightweight and efficient security solution leveraging machine learning and network traffic analysis to detect potential intrusions on a Raspberry Pi network. This system utilizes advanced algorithms, including LSTM neural networks, to provide real-time threat detection and enhance network security
https://github.com/b3ta-blocker/raspberry-pi-based-intrusion-detection-system

ai cybersecurity ids intrusion-detection lstm machine-learning network-security raspberry-pi

Last synced: 12 days ago
JSON representation

Host: GitHub
URL: https://github.com/b3ta-blocker/raspberry-pi-based-intrusion-detection-system
Owner: B3TA-BLOCKER
License: mit
Created: 2024-12-13T07:47:08.000Z (21 days ago)
Default Branch: main
Last Pushed: 2024-12-17T09:59:38.000Z (17 days ago)
Last Synced: 2024-12-17T10:27:39.810Z (17 days ago)
Topics: ai, cybersecurity, ids, intrusion-detection, lstm, machine-learning, network-security, raspberry-pi
Language: Jupyter Notebook
Homepage:
Size: 1.04 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Raspberry Pi-based Intrusion Detection System

This project is a **Raspberry Pi-based Intrusion Detection System (IDS)** that utilizes a Long Short-Term Memory (LSTM) neural network to detect network intrusions. The IDS is trained on the **CSE-CIC-IDS 2018** dataset and is capable of identifying malicious activities in a network.

---

## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Role of the Raspberry Pi](#role-of-the-raspberry-pi)
- [Dataset](#dataset)
- [License](#license)

---

## Overview
Intrusion detection is a critical aspect of cybersecurity, and this project provides a lightweight yet efficient IDS that can be deployed on a Raspberry Pi. By leveraging LSTM-based neural networks, this system detects anomalous patterns in network traffic and classifies them as malicious or benign.

---

## Features
- LSTM neural network for anomaly detection.
- Supports the CSE-CIC-IDS 2018 dataset for training.
- Lightweight implementation for Raspberry Pi deployment.
- Simulation of network traffic using `simulate.py`.
- Easy-to-extend modular architecture.

---

## Project Structure
```
Raspberry-Pi-based-Intrusion-Detection-System/
│
├── app.py # Main application script (Raspberry Pi)
├── CSE-CIC-IDS-2018/ # Dataset directory
├── LICENSE
├── model.keras # Pre-trained LSTM model file (Raspberry Pi)
├── Network_Intrusion_Detection_System_Using_LSTM_Neural_Networks.ipynb
│
├── Project/ # Pre-configured Python virtual environment
├── README.md # Project documentation
└── simulate.py # Simulation script for generating network traffic (Local device)
```

---

## Installation

### For Raspberry Pi
1. **Clone the Repository**
```bash
git clone https://github.com/B3TA-BLOCKER/Raspberry-Pi-based-Intrusion-Detection-System.git
cd Raspberry-Pi-based-Intrusion-Detection-System.git
```

2. **Activate the Pre-configured Virtual Environment**
The project comes with a pre-configured Python virtual environment. Activate it using the following command:
```bash
source Project/bin/activate
```

3. **Prepare the Raspberry Pi**
Ensure the Raspberry Pi is connected to your network. The `app.py` script and `model.keras` file must be present on the Raspberry Pi.

4. **Start the IDS**
Run the IDS by executing:
```bash
python3 app.py
```

### For Local Device
1. Clone the repository on your local device:
```bash
git clone https://github.com/B3TA-BLOCKER/Raspberry-Pi-based-Intrusion-Detection-System.git
cd Raspberry-Pi-based-Intrusion-Detection-System.git
```

2. Use the `simulate.py` script to generate synthetic network traffic. Ensure the Raspberry Pi is running the IDS (`app.py`) and connected to the same network.

---

## Usage

### Running the Application on Raspberry Pi
1. Start the IDS on the Raspberry Pi:
```bash
python app.py
```

### Simulating Network Traffic on Local Device {and make sure to edit the ip address of the rasberry pi in the script!}
Generate synthetic network traffic to test the IDS:
```bash
python simulate.py
```

### Training the Model
If you wish to train a new model, use the provided Jupyter notebook:
```bash
jupyter notebook Network_Intrusion_Detection_System_Using_LSTM_Neural_Networks.ipynb
```

---

## Role of the Raspberry Pi
The Raspberry Pi serves as the **deployment platform** for the Intrusion Detection System. Its role includes:
1. **Processing Network Traffic:**
The Raspberry Pi monitors and processes incoming network traffic for intrusion detection.

2. **Lightweight Deployment:**
The Raspberry Pi’s low power consumption and compact size make it ideal for continuous IDS deployment in small networks.

3. **Running the IDS Model:**
The `app.py` script runs the pre-trained LSTM model (`model.keras`) on the Raspberry Pi to classify traffic as benign or malicious.

4. **Real-Time Detection:**
By deploying the IDS on the Raspberry Pi, network traffic is analyzed in real-time for immediate detection of malicious activities.

---

## Importance of Network Simulation
To test and evaluate the IDS, the `simulate.py` script is used to simulate network traffic from a local device. This ensures that:
1. **Traffic Diversity:**
Simulated traffic includes both benign and malicious samples to test the accuracy of the IDS.

2. **Evaluation of the Model:**
It allows thorough evaluation of the IDS performance without requiring actual malicious activities.

3. **Seamless Testing:**
By simulating network traffic from a local device, developers can test the IDS remotely without affecting live networks.

---

## Dataset

This project uses the **CSE-CIC-IDS 2018** dataset, which is widely used for intrusion detection system training and evaluation. It contains a diverse set of network traffic samples labeled as benign or malicious. For more information on the dataset, visit [CSE-CIC-IDS 2018 Dataset](https://www.unb.ca/cic/datasets/ids-2018.html).

---

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---

## Contribution

Contributions are welcome! If you'd like to contribute, please fork the repository, make your changes, and submit a pull request.

---

## Contact

For any queries or feedback, feel free to contact:
- **Hassaan Ali Bukhari**
- Email: [email protected]
- GitHub: [b3ta-blocker](https://github.com/b3ta-blocker)