A robust approach to authorship verification using siamese deep learning: application in phishing email detection

Given the rapid and significant increase in email data, it is crucial for both individuals and organisations to prioritise the implementation of strong cybersecurity measures to combat attacks such as phishing emails. While continuous research has been made to find the most efficient approach to combating phishing emails, cybercriminals on the other hand continue to be determined, and their techniques become more and more sophisticated. In this paper, we present a novel approach for email classification using a Siamese deep learning network in order to verify authorship. We used the writing style and behavioural characteristics of the authors in this approach to identify the true source of emails and classify them as phishing, legitimate, harassment, or suspicious. This model was evaluated on the SeFACED dataset, where it received an accuracy of 90,12%, showcasing its efficacy in classifying emails, which enhanced email security and contributed to a safer online environment.