A lightweight defense mechanism against next-generation of phishing emails using distilled attention-augmented BiLSTM

This research paper presents a lightweight defense method against advanced phishing emails (fraudulent messages designed to steal information) using a distilled attention-augmented BiLSTM (a type of neural network architecture that learns patterns in sequential data like email text). The approach aims to detect sophisticated phishing attempts more efficiently than existing methods by combining attention mechanisms (which help the AI focus on the most important parts of an email) with a smaller, optimized model.