Spam Shield - Phishing Detection System

Project Overview

Spam Shield is a machine learning-based phishing detection system designed to identify spam in SMS/mail and URLs. The system is built using HTML, CSS, and JavaScript for the frontend, with Python and Flask for backend integration. It also includes a browser extension to provide easy access to the main functionalities.

Functionalities

1. SMS/Mail Spam Detection
2. URL Spam Detection

Datasets

• SMS/Mail Spam Detection: Collected from Kaggle, preprocessed, and trained using the Naive Bayes algorithm.
• URL Spam Detection: Collected from Kaggle, upgraded using the NLTK framework, and trained using Decision Tree, Random Forest, and Multilayer Perceptron algorithms.

Models

• SMS/Mail Spam Detection:
  • Algorithm: Multinomial Naive Bayes (best accuracy)
  • Exported Models: vectorizer.pkl, model.pkl
• URL Spam Detection:
  • Algorithms: Decision Tree, Random Forest, Multilayer Perceptron
  • Framework: TensorFlow
  • Exported Model: model2.pkl

Project Structure

Main Application

• Main File: main.py
• Frontend Files:
  • HTML: html-spamshield.html
  • CSS: css-spamshield.css
• Backend Files:
  • API for URL Detection: API.py
  • URL Feature Extraction: URL_features.py, Feature_extract.py

Browser Extension

• Frontend Files:
  • HTML: html-popup.html
  • CSS: css-styles.css
  • JavaScript: js-popup.js
• Manifest File: manifest.json

Installation and Setup

1. Clone the Repository

   git clone [https://github.com/your-repo/spam-shield.git](https://github.com/Janani-m17/SPAM-SHIELD.git)
   cd spam-shield

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Flask Application

   python main.py

4. Open the Browser Extension

   • Load the extension in your browser via the developer mode.
   • Select the folder containing html-popup.html, css-styles.css, js-popup.js, and manifest.json.

Usage

1. SMS/Mail Spam Detection:

   • Navigate to the website.
   • Input the SMS or mail content in the provided field.
   • Click "Check" to see if the content is spam.

2. URL Spam Detection:

   • Navigate to the website.
   • Input the URL in the provided field.
   • Click "Check" to see if the URL is spam.

3. Browser Extension:

   • Open the extension.
   • Use the same functionalities as the website for quick access.

Contributors

https://github.com/RithikaSundaram

https://github.com/skshrinaya

https://www.github.com/swetha5157

Contact

For any inquiries, please contact janani.mkgs@gmail.com.

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