ShadowWatch – Malicious URL Detection Using Machine Learning

ShadowWatch is a web-based malware detection system that identifies potentially harmful URLs using machine learning techniques. It classifies URLs as malicious or benign based on behavioral patterns and data features.

🔍 Project Overview

• Goal: Detect malicious URLs and display detection type using ML models.
• Type: Full-stack Django application.
• Target Users: General users and security administrators.
• Detection Logic: Based on dataset-driven ML classification of URLs.
• Tech Stack:
  • Backend: Python, Django
  • Frontend: HTML, CSS (basic)
  • ML/DS Libraries: scikit-learn, NumPy, Pandas
  • Database: SQLite
  • Platform: Local + Render (for deployment)

💡 Features

• User and Admin modules
• Predict URL as malicious or benign
• Visualize detection statistics (likes/dislikes charts, detection ratio)
• Trained on real malware datasets
• Download predicted results
• User authentication, registration, and profile viewing

🧠 Machine Learning Flow

• Data cleaning using Pandas & NumPy
• ML models trained on Malware_Datasets.csv
• Output predictions shown with type classification
• Accuracy evaluated with various classification models

🚀 How to Run Locally

1. Clone the repository:

   git clone https://github.com/hari-chintaparthi/ShadowWatch
   cd ShadowWatch

2. Create virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run migrations:

   python manage.py makemigrations
   python manage.py migrate

5. Start server:

   python manage.py runserver

6. Open browser: http://127.0.0.1:8000/

📁 Project Structure

Dark_TRACER_Early_Detection/
│
├── 📁 Dark_TRACER_Early_Detection/        # Main Django config and routing
│   ├── __init__.py
│   ├── asgi.py
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
│
├── 📁 Remote_User/                        # Handles user-side logic
│   ├── 📁 migrations/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── forms.py
│   ├── models.py
│   ├── tests.py
│   └── views.py
│
├── 📁 Service_Provider/                   # Handles admin-side logic
│   ├── 📁 migrations/
│   ├── __init__.py
│   ├── admin.py
│   ├── apps.py
│   ├── models.py
│   ├── tests.py
│   └── views.py
│
├── 📁 Template/                           # Frontend HTML files and assets
│   ├── 📁 Ruser/
│   │   ├── design.html
│   │   ├── get_url_details.html
│   │   ├── login.html
│   │   ├── Predict_Detection_Type.html
│   │   ├── predict_form.html
│   │   ├── ratings.html
│   │   ├── Register1.html
│   │   └── ViewYourProfile.html
│   ├── 📁 Sprovider/
│   │   ├── charts.html
│   │   ├── charts1.html
│   │   ├── design1.html
│   │   ├── dislikeschart.html
│   │   ├── Download_Predicted_DataSets.html
│   │   ├── Find_Malware_Detection_Type_Ratio.html
│   │   ├── likeschart.html
│   │   ├── Predict_Malware_Detection_Type_Details.html
│   │   ├── serviceproviderlogin.html
│   │   ├── train_model.html
│   │   └── View_Remote_Users.html
│   └── 📁 images/
│       ├── bg.jpg
│       ├── Login.jpg
│       └── Register.jpg
│
├── 📁 Database/                           # DB helpers or raw data
├── Labeled_Data.csv                      # Cleaned/labeled dataset
├── Malware_Datasets.csv                  # Raw malware dataset
├── build.sh                              # Build or deploy script (optional)
├── manage.py                             # Django project manager
├── render.yaml.txt                       # Render platform config
├── requirements.txt                      # Python dependency file
└── .gitignore                            # Git ignore rules

🌐 GitHub Repository

🔗 ShadowWatch on GitHub