# An Explainable Transformer-based Model for Phishing Email Detection: A Large Language Model Approach

arXiv: https://arxiv.org/abs/2402.13871 · HTML: https://mlsanity.com/paper/2402.13871/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["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)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Understanding AdamW through Proximal Methods and Scale-Freeness](https://mlsanity.com/paper/2202.00089/) (2022)
- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [URLTran: Improving Phishing URL Detection Using Transformers](https://mlsanity.com/paper/2106.05256/) (2021)
- [An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach](https://mlsanity.com/paper/2311.04913/) (2023)
- [CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails](https://mlsanity.com/paper/2010.03484/) (2020)
- [Evaluation of Federated Learning in Phishing Email Detection](https://mlsanity.com/paper/2007.13300/) (2020)
- [An intelligent classification model for phishing email detection](https://mlsanity.com/paper/1608.02196/) (2016)
- [Interpretable Bangla Sarcasm Detection using BERT and Explainable AI](https://mlsanity.com/paper/2303.12772/) (2023)
- [Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection Zero-day Phishing Email](https://mlsanity.com/paper/1302.0629/) (2013)

## Built on by (most cited first, 9 of 9)

- [Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements](https://mlsanity.com/paper/2502.12904/) (2025)
- [Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](https://mlsanity.com/paper/2409.09989/) (2024)
- [ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis](https://mlsanity.com/paper/2405.08026/) (2024)
- [SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection](https://mlsanity.com/paper/2406.06663/) (2024)
- [Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability](https://mlsanity.com/paper/2506.13746/) (2025)
- [Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets](https://mlsanity.com/paper/2509.24080/) (2025)
- [Every Character Counts: From Vulnerability to Defense in Phishing Detection](https://mlsanity.com/paper/2509.20589/) (2025)
- [Phishing Email Detection Using Large Language Models](https://mlsanity.com/paper/2512.10104/) (2025)
- [Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling](https://mlsanity.com/paper/2602.19087/) (2026)

## Abstract (arXiv)

Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit technological advancements and sophistication to make detection and prevention of phishing more challenging. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. Large Language Models (LLMs) and Masked Language Models (MLMs) possess immense potential to offer innovative solutions to address long-standing challenges. In this research paper, we present an optimized, fine-tuned transformer-based DistilBERT model designed for the detection of phishing emails. In the detection process, we work with a phishing email dataset and utilize the preprocessing techniques to clean and solve the imbalance class issues. Through our experiments, we found that our model effectively achieves high accuracy, demonstrating its capability to perform well. Finally, we demonstrate our fine-tuned model using Explainable-AI (XAI) techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Transformer Interpret to explain how our model makes predictions in the context of text classification for phishing emails.