# A Robust and Explainable Transformer-Based Framework for Phishing Email Detection

arXiv: https://arxiv.org/abs/2511.12085 · HTML: https://mlsanity.com/paper/2511.12085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 15 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities](https://mlsanity.com/paper/2405.12750/) (2024)
- [Adversarial Texts with Gradient Methods](https://mlsanity.com/paper/1801.07175/) (2018)
- [Impact of Adversarial Training on Robustness and Generalizability of Language Models](https://mlsanity.com/paper/2211.05523/) (2022)
- [Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection](https://mlsanity.com/paper/2405.11619/) (2024)
- [A Text Classification Model Combining Adversarial Training with Pre-trained Language Model and neural networks: A Case Study on Telecom Fraud Incident Texts](https://mlsanity.com/paper/2411.06772/) (2024)

## Abstract (arXiv)

Phishing and related cyber threats are becoming increasingly sophisticated, with email-based phishing remaining the most persistent attack vector. These attacks exploit human vulnerabilities to deliver malware or gain unauthorized access to sensitive information. Transformer-based models enhance phishing detection through robust contextual language understanding; yet they are often regarded as black boxes due to a lack of interpretability. Moreover, recent AI-enabled attacks further undermine model resilience. To address these challenges, this work proposes a lightweight phishing detection framework based on DistilBERT, a lightweight Transformer model. Robustness to embedding-level perturbations and character-level input noise is enhanced through gradient-based adversarial training using the Fast Gradient Method (FGM), combined with stochastic character-level perturbations. To improve transparency, three prominent Explainable AI (XAI) methods, LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and IG (Integrated Gradients), are integrated to interpret model decision-making. A structured rule-based prompt combines model predictions and XAI features to guide Flan-T5-Small in generating plain-language, evidence-based explanations. Experimental results demonstrate that the proposed framework outperforms a standard DistilBERT-based detection model trained without robustness enhancements in terms of accuracy and resilience. This integrated approach helps bridge the gap between model reliability and user trust, advancing transparent phishing detection.