Mahdi-s / High-powered-jamming-simulation

A simulation of high-powered jamming attacks on ADS-B devices using MATLAB, and detection using a machine learning model in Python. Features a Random Forest Classifier for attack identification and includes detailed instructions for running the simulation.

📡 ADS-B Jamming Simulation and ML Detection 🛩️

🎯 Project Overview

This project combines a MATLAB simulation of high-powered jamming attacks on ADS-B devices with a Python-based machine learning model for detecting such attacks.

🔬 MATLAB Simulation

The MATLAB component simulates a communication system under jamming conditions, focusing on:

• AWGN channel modeling
• SNR, EVM, and Eye Diagram feature extraction
• CRC-based error detection

🤖 Machine Learning Model

The Python script implements a Random Forest Classifier to detect jamming attacks based on the features extracted from the MATLAB simulation.

🚀 Getting Started

Prerequisites

• MATLAB (version R2019b or later recommended)
• Python 3.7+
• Required Python libraries: numpy, pandas, scikit-learn

🛠️ Usage

1. Run the MATLAB simulation:

CommunicationSimulator

This will generate the EVMdata.xlsx file.

2. Rename EVMdata.xlsx to Data.csv.

3. Run the Python ML model:

python MLmodel.py

📊 Features

The ML model uses the following features extracted from the MATLAB simulation:

features = ['RMSEVM', 'MAXEVM', 'EYEAMP', 'EYESNR', 'EYEDELAY', 'EYEWIDTH', 'ENERGY', 'BPR', 'MEANEIGEN']

🔍 Results

The ML model evaluates different combinations of features and outputs the top results based on accuracy. Results are saved in results.csv.

📚 Further Reading

For more information on the underlying concepts, please visit: IEEE Paper

🙏 Acknowledgements

• IEEE for the original paper
• Contributors and maintainers of the scikit-learn library