A hybrid approach to phishing email detection: leveraging machine learning and explainable artificial intelligence

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Abstract
With the increasing use of emails in our daily lives, they have become a prime target of phishing attacks, posing a significant threat to users. Attackers pretend to be trusted sources and use email phishing attacks to trick people into clicking malicious links or opening attachments. The aim of these attacks is to obtain sensitive information, such as financial information, login credentials, and personally identifiable information. Emails have attributes including the URL, sender, subject, receiver(s), and body. This paper proposes a hybrid intelligence model that integrates machine learning algorithms (ML) and natural language processing (NLP) techniques for email phishing detection. Three ML algorithms are employed: logistic regression, decision tree, and random forest. In addition, a customized ChatGPT model has been developed to receive email classification results from the hybrid model. This model educates users on recognizing phishing emails by explaining email classifications, highlighting keywords, and offering security tips. The proposed approach to detecting phishing emails raises awareness and educates users on recognizing and reporting email phishing attacks.

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