Threat Snare

A Malicious URL Detection App

ThreatSnare is a lightweight and intelligent web-based tool built using Flask and Machine Learning that analyzes and detects potentially malicious, phishing, defacement, or malware-infected URLs in real-time.

It uses an ML model trained on URL-based features and provides instant feedback on whether a website is safe or unsafe.

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🚀 Features

• 🧠 Machine Learning Model (LightGBM) trained on real-world malicious URL datasets.
• ⚡ Instant URL Prediction — just enter a link and get classified results.
• 🌐 Smooth & Responsive Web Interface built with Flask and Tailwind CSS.
• 🔒 Session-based results (no persistent storage).
• ☁️ Deployed on Render with a custom domain and real-time monitoring via UptimeRobot.

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🧩 Tech Stack

Component	Technology
Frontend	HTML, Tailwind CSS
Backend	Flask (Python)
ML Model	LightGBM
Deployment	Render
DNS Hosting	InfinityFree (custom domain)

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🧠 Model Overview

The app uses URL-based feature extraction — such as the number of dots, presence of IP address, suspicious keywords, URL length, and other heuristics — to generate a feature vector for prediction.
The model then classifies URLs into one of four categories:

Label	Meaning
SAFE	No threat detected
DEFACEMENT	Suspicious modifications
PHISHING	Credential stealing attempt
MALWARE	Harmful or infected link

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🛠️ Setup (Local Development)

1. Clone the repository

git clone https://github.com/KnOX-07/ThreatSnare.git
cd ThreatSnare

2. Create a virtual environment (if you prefer)

python -m venv venv
source venv/bin/activate   # (on macOS/Linux)
venv\Scripts\activate      # (on Windows)

3. Install dependencies

pip install -r requirements.txt

4. Run the Flask app

python app.py

App will be available at: http://127.0.0.1:5000

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🧑‍💻 Author

Rupal Mandal
📂 GitHub: @_ruP4L
🌐 Project: ThreatSnare
📧 Email: rupalmandal009@gmail.com