Malware_detection

Malware detection full stack project for the users

🛡️ Malware Detection System

A full-stack malware classification project using Machine Learning & Modern Web Technologies.

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📌 Overview

This project is designed to detect malicious vs. benign files using machine learning models trained on a custom malware dataset.
It also includes a frontend UI and backend API so users can upload files and receive predictions in real-time.

This repository contains:

• ✔️ Machine Learning model for malware detection
• ✔️ Full dataset (CSV format)
• ✔️ Training script (train.py)
• ✔️ Backend API server
• ✔️ Frontend interface
• ✔️ Confusion matrix and evaluation results
• ✔️ Setup scripts and project structure

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🚀 Features

• Binary classification (Malware vs Benign)
• Custom dataset with expanded features
• Machine Learning pipeline (preprocessing → training → prediction)
• Visualization of evaluation metrics
• REST API backend for prediction
• React/Node frontend for file upload + result display
• 100% open-source

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🧠 Machine Learning Model

Model used:

• XGBoostClassifier
• StandardScaler for normalization
• 80/20 train-test split
• Evaluation metrics:
  • Accuracy
  • Precision / Recall / F1-score
  • Confusion matrix (saved as confusion_matrix.png)

Training script:

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📁 Project Structure

Malware_detection/ │ ├── backend/ # Backend API server ├── malware-frontend/ # Frontend application ├── confusion_matrix.png # Model performance plot ├── malware_dataset_expanded.csv ├── malware_dataset_gmm_5000.csv │ ├── train.py # Model training script ├── run.sh # Script to run backend + frontend ├── package.json # Node project file ├── .gitignore └── README.md

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⚙️ Installation & Usage

✅ Clone the Repository

git clone https://github.com/Harry-Khatri/Malware_detection.git
cd Malware_detection

For Model Training
python3 train.py

cd backend
npm install
npm start
cd malware-frontend
npm install
npm run dev