Phishing Email Detection using Machine Learning & Deep Learning

Project Overview

This project implements two approaches for phishing email detection:

ML-Based Approach

• Uses traditional Machine Learning algorithms (e.g., Logistic Regression, Random Forest, SVM).
• Extracts features like email headers, content analysis, and metadata.

DL-Based Approach

• Uses Deep Learning models (e.g., LSTMs, CNNs) for text-based email classification.
• Leverages word embeddings (TF-IDF, Word2Vec, BERT) for email analysis.

Project Structure

 PhishingEmailDetection
 ├──  DL_based_end_to_end_phishing_email_detection.ipynb  # Deep Learning model
 ├──  ML_based_end_to_end_phishing_email_detection.ipynb  # Machine Learning model
 ├──  README.md  # Project documentation

Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/Srikanth-coder-max/PhishingEmailDetection.git
cd PhishingEmailDetection

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run Jupyter Notebook

jupyter notebook

4️⃣ Open and execute the notebook (.ipynb)

Models & Techniques Used

• Feature Engineering: Email metadata, text features
• Machine Learning Models: Logistic Regression, SVM, Random Forest
• Deep Learning Models: LSTM, CNN, Transformers (if used)
• Performance Metrics: Accuracy, Precision, Recall, F1-Score

Results & Insights

• The ML-based model performs well with structured data.
• The DL-based model excels in analyzing textual content.
• Combining both approaches can further improve phishing detection accuracy.

Future Improvements

• Fine-tune Deep Learning models (e.g., BERT, GPT)
• Deploy as a Flask/Streamlit Web App
• Integrate real-time email classification

Contributions & Feedback

Pull requests and suggestions are welcome! Feel free to open an issue or reach out.

GitHub Repo: PhishingEmailDetection