Explainable Transformer-Based Email Phishing Classification with Adversarial Robustness

TL;DR

This paper introduces a hybrid transformer-based framework for email phishing detection that combines adversarial training and explainability techniques to improve robustness and transparency against AI-generated threats.

Contribution

It proposes a novel approach integrating DistilBERT with adversarial training and LIME XAI, along with Flan-T5 for user-friendly explanations, enhancing phishing detection robustness and interpretability.

Findings

Improved resilience of phishing detection against adversarial attacks.

Enhanced transparency through LIME explanations.

Effective generation of plain-language security narratives.

Abstract

Phishing and related cyber threats are becoming more varied and technologically advanced. Among these, email-based phishing remains the most dominant and persistent threat. These attacks exploit human vulnerabilities to disseminate malware or gain unauthorized access to sensitive information. Deep learning (DL) models, particularly transformer-based models, have significantly enhanced phishing mitigation through their contextual understanding of language. However, some recent threats, specifically Artificial Intelligence (AI)-generated phishing attacks, are reducing the overall system resilience of phishing detectors. In response, adversarial training has shown promise against AI-generated phishing threats. This study presents a hybrid approach that uses DistilBERT, a smaller, faster, and lighter version of the BERT transformer model for email classification. Robustness against text-based adversarial perturbations is reinforced using Fast Gradient Method (FGM) adversarial training. Furthermore, the framework integrates the LIME Explainable AI (XAI) technique to enhance the transparency of the DistilBERT architecture. The framework also uses the Flan-T5-small language model from Hugging Face to generate plain-language security narrative explanations for end-users. This combined approach ensures precise phishing classification while providing easily understandable justifications for the model's decisions.