🚨 AI-powered phishing detection system 🚨

⚙️ About the Project

An AI-powered phishing detection system using machine learning to classify emails as phishing or safe. Built using Python, Streamlit, and scikit-learn, this system analyzes email bodies and URLs to detect phishing attempts.

🚀 Features

• Email Body Analysis: Uses text processing to identify phishing patterns.
• URL Scanning: Extracts and evaluates URLs within the email to detect malicious links.
• Machine Learning: Trained with labeled phishing data to make predictions on email content.
• Interactive UI: Built with Streamlit for real-time predictions.

🧑‍💻 Technologies Used

• Python 🐍
• Streamlit 🌐
• scikit-learn 🤖
• pandas 📊
• nltk 🧠
• Requests 🌍
• BeautifulSoup 🍲

🎯 Installation & Setup

1. Clone the repository

   git clone https://github.com/yourusername/AI-Powered-Phishing-Detection-System.git
   cd AI-Powered-Phishing-Detection-System
   

2. Install Dependencies Create and activate a virtual environment, then install required packages:

   python -m venv venv
   source venv/bin/activate   # For macOS/Linux
   venv\Scripts\activate      # For Windows
   
   pip install -r requirements.txt
   

3. Run the App Start the Streamlit app to launch the phishing detection system:

   streamlit run phishing_detection.py
   

4. Upload Dataset for Training (Optional) Upload a CSV file with columns email_body (content of the email) and label (1 for phishing, 0 for safe) to train the model.

🔍 How to Use

• Training the Model: Upload a CSV file containing emails labeled as phishing or safe.
• Prediction: Enter the body of an email, and the system will classify it as phishing or safe.

Example Email to Test:

• Phishing Email:

  Congratulations! You've won a $1000 gift card. Click here to claim your prize: http://phishing.com
  

• Safe Email:

  Hey, just checking in on our project. Let me know your availability for a meeting.
  

5. Check Prediction Click Check Phishing to see the prediction results for the email you entered.

📊 Results

The system will display whether the email is a phishing attempt or safe based on its body content and URL features.

🛠️ Contributing

Contributions are welcome! If you'd like to improve the system or add new features, feel free to fork the repository and submit a pull request.

🙌 Acknowledgements

• Streamlit for creating the amazing interface.
• scikit-learn for machine learning tools.
• Shields.io for badges.
• FontAwesome for awesome icons.

💻 Preview

Phishing Email:

Safe Email:

🌐 Connect with Me

• 📧 Email
• 💼 LinkedIn