https://github.com/priyanujbora/black-hole-attack-in-manets-with-streamlit-implementation
Interactive simulation of black hole attack detection in Mobile Ad-hoc Networks (MANETs) using novel Dolphin-Bee optimization algorithm with Streamlit visualization interface
https://github.com/priyanujbora/black-hole-attack-in-manets-with-streamlit-implementation
ad-hoc-networks bee-colony-optimization bio-inspired-algorithms black-hole-attack dolphin-optimization manet network-routing network-security network-simulation python streamlit
Last synced: 3 months ago
JSON representation
Interactive simulation of black hole attack detection in Mobile Ad-hoc Networks (MANETs) using novel Dolphin-Bee optimization algorithm with Streamlit visualization interface
- Host: GitHub
- URL: https://github.com/priyanujbora/black-hole-attack-in-manets-with-streamlit-implementation
- Owner: PriyanujBora
- License: mit
- Created: 2025-02-27T12:48:44.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2025-02-27T17:06:05.000Z (3 months ago)
- Last Synced: 2025-02-27T18:58:38.529Z (3 months ago)
- Topics: ad-hoc-networks, bee-colony-optimization, bio-inspired-algorithms, black-hole-attack, dolphin-optimization, manet, network-routing, network-security, network-simulation, python, streamlit
- Language: Python
- Homepage: https://bha-manet-simulation.streamlit.app/
- Size: 30.3 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Black Hole Attack Detection in MANETs using Dolphin-Bee Optimization
## Overview
This project implements a simulation of Mobile Ad-hoc Networks (MANETs) with black hole attack detection using a hybrid Dolphin-Bee optimization algorithm. The project includes both a simulation module and a Streamlit-based interactive web interface for visualizing and analyzing the detection process.
## Features
- **MANET Simulation**: Simulates a dynamic mobile ad-hoc network with configurable parameters
- **Black Hole Attack Modeling**: Implements malicious nodes that drop packets in the network
- **Hybrid Dolphin-Bee Optimization**: Novel approach combining dolphin echolocation and bee colony optimization for route optimization and attack detection
- **Interactive Visualization**: Streamlit-based UI to visualize network topology, attack detection, and performance metrics
- **Performance Analysis**: Tracks and displays network performance metrics including packet delivery ratio, energy consumption, and detection accuracy
## Technologies Used
- Python 3.x
- Streamlit for web interface
- NumPy for numerical computations
- Matplotlib for data visualization
- NetworkX for graph operations and network modeling
## Installation
1. Clone the repository:
```bash
git clone https://github.com/PriyanujBora/Black-Hole-Attack-in-MANETs-with-Streamlit-Implementation.git
cd Black-Hole-Attack-in-MANETs-with-Streamlit-Implementation
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
### Running the Simulation
To run the basic simulation without the web interface:
```bash
python manet_dolphin_bee_simulation.py
```
### Running the Streamlit Web Application
To launch the interactive web application:
```bash
streamlit run streamlit_implementation.py
```
The web interface allows you to:
- Adjust network parameters (number of nodes, communication range, etc.)
- Control the percentage of malicious nodes
- Visualize the network in real-time
- Track detection metrics
- Analyze simulation results through various charts
## How It Works
1. **Network Initialization**: Creates a MANET with randomly placed nodes, some of which are malicious
2. **Traffic Simulation**: Simulates packet transmission between nodes
3. **Dolphin-Bee Optimization**:
- Dolphin phase: Uses echolocation-inspired algorithm to explore potential routes
- Bee phase: Uses bee colony optimization to exploit and refine routes
4. **Blackhole Detection**: Identifies malicious nodes based on behavioral analysis and network metrics
For a detailed explanation of the Dolphin-Bee Optimization algorithm and the blackhole detection process, see the [Algorithm Documentation](ALGORITHM.md).
## Project Structure
- `manet_dolphin_bee_simulation.py`: Core simulation module
- `streamlit_implementation.py`: Interactive web application
- `requirements.txt`: Project dependencies
## Future Work
- Implementation of other attack types (wormhole, sinkhole)
- Integration of additional optimization algorithms
- Performance comparison with traditional detection methods
- Real-time mobile device simulation
## License
[MIT License](LICENSE)
## Contributors
[Priyanuj Bora](https://github.com/PriyanujBora), [Fahim Mashud Barbhuiyan](https://github.com/Fahim98), [Rohan Jaiswal](https://github.com/RohanJaiswall) - Main Developers