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:

   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:

   pip install -r requirements.txt

Usage

Running the Simulation

To run the basic simulation without the web interface:

python manet_dolphin_bee_simulation.py

Running the Streamlit Web Application

To launch the interactive web application:

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.

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

Contributors

Priyanuj Bora, Fahim Mashud Barbhuiyan, Rohan Jaiswal - Main Developers