Phishing attacks remain one of the most significant cybersecurity challenges, tricking users into disclosing sensitive information through deceptive emails. The Advanced Phishing Detection Suite leverages state-of-the-art deep learning techniques to automatically classify emails as either legitimate or phishing attempts, helping organizations and individuals safeguard their communication channels. 🔐
In today’s digital landscape, phishing messages evolve continuously, rendering manual detection increasingly unreliable. This project leverages advanced deep learning models to analyze email text and detect phishing attempts with high accuracy. By combining robust natural language processing (NLP) techniques with modern neural architectures, our solution addresses the nuances of phishing language and tactics. 💡
- Deep Learning Powered 🤖: Utilizes cutting-edge deep learning models (e.g., LSTM, CNN, or Transformer-based architectures) for efficient phishing detection.
- Automated Classification ⚡: Classifies emails into “Legitimate” or “Phishing” categories in real time.
- NLP Preprocessing 📝: Integrates comprehensive text preprocessing steps including tokenization, embedding generation, and sequence modeling.
- Modular & Scalable 🔄: Designed as a standalone suite that can be integrated into larger email security systems.
- Performance Metrics 📊: Evaluates model performance using metrics such as Accuracy, Precision, Recall, F1-Score, and ROC-AUC.
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Model Architecture:
Our system can be configured to use various deep learning models depending on the use case. The architecture is designed to capture both local and contextual features in email text. -
Data Preprocessing:
Prior to classification, emails undergo cleaning, tokenization, and vectorization. You can integrate pre-trained embeddings (e.g., GloVe or FastText) or use transformer-based embeddings to capture semantic nuances. -
Training Framework:
Developed in Python using popular libraries such as TensorFlow or PyTorch. The training pipeline is built for both rapid experimentation and scalability. -
Evaluation:
Model performance is continuously evaluated using a dedicated test set, ensuring robustness against evolving phishing strategies.
For more detailed technical documentation, please refer to the project docs within the repository.
To get started with the Advanced Phishing Detection Suite, follow these steps:
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Clone the Repository:
git clone https://github.com/itaim18/Advanced-Phishing-Detection-Suite.git cd Advanced-Phishing-Detection-Suite -
Set Up a Virtual Environment:
python3 -m venv venv source venv/bin/activate # For Windows: venv\Scripts\activate
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Install Dependencies:
pip install -r requirements.txt
Ensure that you are using Python 3.7 or later.
After installation, you can run the training and inference scripts provided:
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Training the Model:
python train.py --data path/to/your/dataset.csv
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Running Inference on New Emails:
python infer.py --email "Paste the email content here"
For further customization, refer to the inline comments and documentation within the codebase.
Initial experiments have shown promising results, with the suite achieving high accuracy in distinguishing phishing emails from legitimate ones. Detailed performance metrics (such as Precision, Recall, and F1-Score) are provided in the documentation and can be tailored to your dataset and application requirements.
For an in-depth overview and demonstration of the system, please view the presentation slides here: