Phishing Email Detection Model Using Deep Learning

Phishing via email continues to be a concern in the cybersecurity realm as it exposes both individuals and companies to levels of risk due to clever social engineering methods employed by cybercriminals. This study introduces an extensive deep learning strategy for identifying phishing emails using a blend of various models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The suggested approach utilizes natural language processing and learning techniques to evaluate and categorize email material by identifying subtle attributes that differentiate valid from harmful communication effectively. Through testing and careful evaluation of the model performance, achieved results; the LSTM model attained a notable accuracy of 99.92% in detecting phishing emails. This progress highlights the promise of learning methods in addressing complex phishing threats and improving email security measures. The research offers insights into the effectiveness of neural network approaches in detecting phishing emails by comparing the performance of multiple deep learning architectures. The high level of accuracy attained indicates a path for creating smarter email security solutions that can evolve to counter cyber threats effectively.