CyberSentinel – AI-Powered Phishing Detection System

CyberSentinel is a full-stack cybersecurity web application that detects malicious URLs using a hybrid approach combining:

• Machine Learning (Random Forest)
• VirusTotal API
• Content Analysis
• Community Feedback + Retraining

It also includes a Chrome Extension for real-time protection.

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Screenshots

Login Page

Home Page

Output

Report URL (Feedback)

Feedback Review Panel

QR Detection

URL Analytics

Analytics Using Visual Representation

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Features

• Real-time URL phishing detection
• Hybrid analysis using ML, API, and content analysis
• Admin panel for feedback review
• Model retraining system
• Chrome extension integration
• JWT-based authentication
• QR code URL analysis

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Tech Stack

Frontend

• React (Vite)
• Tailwind CSS

Backend

• Flask (Python)
• Flask-JWT-Extended

Machine Learning

• scikit-learn
• pandas
• numpy

Database

• SQLite / PostgreSQL

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Installation Guide

1. Clone the Repository

git clone https://github.com/yourusername/CyberSentinel.git
cd CyberSentinel

2. Setup Backend

cd backend

Windows

python -m venv venv
venv\Scripts\activate

Mac/Linux

python3 -m venv venv
source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Download Dataset and Train Model

python ml/download_dataset.py
python ml/train_model.py

Add Environment Variables

Create a .env file inside the backend folder:

VIRUSTOTAL_API_KEY=your_api_key_here
SECRET_KEY=your_secret_key

Get a VirusTotal API key from: VirusTotal

Start Backend Server

python app.py

Backend will run on: http://localhost:5000

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3. Setup Frontend

Open a new terminal and go to the root folder:

cd ..
npm install
npm run dev

Frontend will run on: http://localhost:5173

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4. Chrome Extension Setup

1. Open Chrome and go to chrome://extensions/.
2. Enable Developer Mode.
3. Click Load Unpacked.
4. Select the extension folder from this project.

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Default Admin Login

• Username: admin
• Password: admin123

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How It Works

1. User enters a URL or visits a website.
2. Extension or frontend sends the URL to the backend.
3. Backend performs:
   • ML prediction
   • VirusTotal check
   • Content analysis
4. Results are combined into a threat score.
5. Final verdict is returned: Benign, Suspicious, or Phishing.

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Model Retraining

• Users submit feedback.
• Admin approves feedback.
• Approved data is added to the dataset.
• Model is retrained using:

POST /api/admin/retrain-model

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Authors

Mohataseem Khan

• LinkedIn: Profile
• GitHub: Mohataseem89

Rehan Khan

• LinkedIn: Profile
• GitHub: RehanKhan1704

Saad Shaikh

• LinkedIn: Profile
• GitHub: SS07158

Ansari Husain

• LinkedIn: Profile
• GitHub: 71-husain