Abstract

Phishing attacks, particularly spear-phishing, represent a significant threat to organizational cybersecurity. Traditional rule-based or keyword detection methods are no longer sufficient to detect increasingly sophisticated attacks. This paper presents a deep learning approach utilizing Long Short-Term Memory (LSTM) networks to detect spear-phishing emails based on their content. We preprocess and analyze the textual body of emails using natural language processing techniques and demonstrate how an LSTM-based model can effectively classify phishing and legitimate emails. The experimental results on a sample dataset show promising performance in terms of accuracy and robustness. Our work highlights the potential of deep learning in real-world cybersecurity applications and suggests future avenues for enhancing phishing detection capabilities. To further enhance detection capabilities, future research paths will examine transformer-based designs like BERT and incorporate multimodal characteristics like email headers, metadata, and embedded links. This work lays the groundwork for creating more robust anti-phishing systems and adds to the expanding corpus of research on AI-driven cybersecurity solutions.