🛡️ Phishing Email Detector — Chrome Extension

A machine learning-powered Chrome extension that detects phishing emails in real-time. Built as part of coursework at San José State University (CMPE 255 — Data Mining).

🎯 Overview

Phishing attacks remain one of the most common cybersecurity threats. This extension analyzes email content directly in the browser using a pre-trained ML model to classify emails as safe or phishing — giving users instant visual feedback.

✨ Features

• Real-Time Detection — Scans email content on the active page
• ML-Powered — Uses a scikit-learn model trained on 18,000+ labeled phishing/legitimate emails
• Chrome Extension — Manifest V3, lightweight popup UI
• Pre-trained Model — Serialized phishing_model.pkl and vectorizer.pkl ready to use
• Visual Alerts — Clear popup interface showing phishing probability

🏗️ Architecture

PhishingDetectorExtension/
├── manifest.json          # Chrome Extension config (Manifest V3)
├── popup.html             # Extension popup UI
├── popup.js               # Popup logic & API calls
├── background.js          # Service worker for background tasks
├── main.js                # Content script injected into web pages
├── model/
│   ├── phishing_model.pkl # Trained ML classification model
│   └── vectorizer.pkl     # TF-IDF text vectorizer
├── src/
│   └── Main.java          # Model training pipeline
└── Phishing_Email.csv     # Dataset (18,000+ samples)

🛠️ Tech Stack

Layer	Technology
Extension	Chrome Manifest V3, JavaScript, HTML/CSS
ML Model	Python, scikit-learn, TF-IDF Vectorization
Training	Java (data preprocessing), Jupyter Notebook
Dataset	18,000+ labeled phishing/legitimate emails (CSV)

🚀 Getting Started

Load the Extension in Chrome

1. Clone this repo:

   git clone https://github.com/amanimran786/PhishingDetectorExtension.git

2. Open Chrome → chrome://extensions/
3. Enable Developer Mode (toggle in top-right)
4. Click "Load unpacked"
5. Select the PhishingDetectorExtension folder
6. The extension icon will appear in your toolbar

Usage

1. Navigate to a page with email content
2. Click the extension icon in the Chrome toolbar
3. The popup displays the phishing risk assessment

📊 Model Details

• Algorithm: scikit-learn classifier with TF-IDF feature extraction
• Dataset: Phishing_Email.csv — 18,000+ labeled samples
• Features: Text content vectorized using TF-IDF
• Output: Binary classification (Phishing / Legitimate) with confidence score

👨‍💻 Author

Aman Imran — B.S. Software Engineering, San José State University

• 📧 aman.imran@sjsu.edu
• 🔗 LinkedIn
• 🌐 Portfolio

📄 License

This project is open source and available for educational purposes.