Malicious URLs Detection using Machine Learning

Table of Contents

• Overview
• Directory Structure
• Architecture
• Installation
• Usage

Overview

This project focuses on developing a machine learning-based system for detecting malicious URLs, which are often used in phishing, malware distribution, and other cyberattacks. The goal is to create a tool capable of classifying URLs as either benign or malicious based on patterns identified through training models.

Directory Structure

├── webapp/
│   ├── app.py                      # Flask application
│   ├── templates/
│   │   └── index.html              # HTML template
│   └── static/
│       ├── style.css               # Stylesheet            
│       ├── script.js               # JavaScript for frontend
│       └── image.jpg
├── src/
│   ├── __init__.py                 # Marks src as a package
│   └── Extraction_Functions.py     # Functions for feature extraction
├── Dataset.csv                     # Dataset used for model training
├── model.pkl                       # Pre-trained machine learning model
├── Malicious_URLs_Detection.ipynb  # Jupyter Notebook for model training
├── requirements.txt                
└── README.md                       # Project documentation

Architecture

Installation

Follow these steps to set up the project environment and install the required dependencies:

1. Clone the Repository

   git clone https://github.com/Youssef-Daouayry/Malicious-URLs-Detection-using-Machine-Learning

2. Navigate to the project directory

   cd Malicious-URLs-Detection-using-Machine-Learning

3. Install Dependencies

   pip install -r requirements.txt

Usage

You can now run the Flask application. Use the following command to start the web server:\

python webapp/app.py

Open your web browser and navigate to http://localhost:5000 to use the application.