## 2 Related Work  
Detecting phishing emails has been a significant focus of research within the cybersecurity domain, driven by these attacks' increasing sophistication and prevalence. Numerous studies have investigated using ML and DL approaches to enhance the detection and classification of phishing emails. In this section, we review key studies that leverage these techniques to address the challenges posed by phishing emails. The key findings from the literature are summarized in Table 1.  
[PERSON] et al. [31] highlighted the growing cyber threat of phishing attacks and explored machine learning-based phishing email detection. The phishing email dataset used in their paper is a curated dataset of public datasets such as SpamAssassin and datasets from the UCI Machine Learning Library. Several models are trained to detect phishing emails, including CNN, XGBoost, RNN, SVM, and a BERT-LSTM hybrid model. Based on the performance evaluation, the BERT-LSTM model has the best results, with an accuracy of 99.55% and an F1-score of 99.24%. In [32], the authors compared traditional ML models (i.e., logistic regression, random forest, SVM, and naive Bayes) with transformer models (i.e., distilBERT, BERT, XLNet, RoBERTa, and ALBERT) for phishing email detection using a dataset of 119,148 emails from the Enron dataset and multiple phishing repositories. Results show that RoBERTa achieved the highest accuracy 99.43%, while SVM outperformed traditional ML models with 98.76% accuracy. Transformer-based models reduced misclassifications by 53.7% but struggled with mixed-language content. In [12], the authors developed a web-based AI platform for detecting phishing emails, employing ML models such as Support Vector Machine (SVM), multinomial naive Bayes, \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Ref.** & **Year** & **Dataset** & **Model (s)** & **Result (Accuracy)** \\ \hline
[31] & 2025 & Public datasets (UCI, SpamAssassin) & CNN, XGBoost, RNN, SVM, BERT-LSTM & 99.55\% \\ \hline
[32] & 2024 & Enron and multiple phishing & Logistic Regression, Random Forest, SVM, & \\  & & repositories & Naive Bayes, distilBERT, BERT, XLNet, & \\  & & & RoBERTa, ALBERT & \\ \hline
[12] & 2024 & Merged (6 datasets) & SVM, Naive Bayes, Random Forest & 99.19\% \\ \hline
[22] & 2024 & Kaggle phishing dataset & DistilBERT & 98.48\% \\ \hline
[33] & 2023 & Public datasets (UCI, Kaggle) & CNN, RNN, LSTM, BERT & 99.61\% \\ \hline
[26] & 2023 & Nazario phishing corpus & ANN, RNN, CNN & 99.51\% \\ \hline
[13] & 2022 & SpamAssassin & LSTM, Gaussian Naive Bayes & 97.18\% \\ \hline
[23] & 2022 & N/A & BERT, DistilBERT & 99\% \\ \hline
[14] & 2021 & Custom dataset & CNN, LSTM & 96.34\% \\ \hline
[34] & 2021 & Fraud dataset & GCN & 98\% \\ \hline
[15] & 2020 & SMS Spam Collection v.1 & SVM, Decision Tree, Random Forest & 98.77\% \\ \hline
[35] & 2019 & IWSPA-AP 2018 & RCNN & 99.848\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Related Work on ML and DL Techniques for Phishing Email Detectionand random forest. These models used Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. Their merged dataset, combining six sources, contained approximately 82,500 emails, with SVM achieving the highest accuracy of 99.19%  
Recent work [22] proposed a transformer-based approach using a fine-tuned DistilBERT model with explainable AI (XAI) techniques. A phishing dataset from Kaggle was balanced using resampling techniques, resulting in a testing accuracy of 97.50% on the imbalanced dataset and 98.48% on the balanced dataset. However, this study did not compare the model to other state-of-the-art methods on the same dataset. Similarly, DL techniques such as CNNs, RNNs, LSTMs, and BERT were employed in [33] to identify phishing emails. Publicly available datasets were obtained from the UCI ML Repository, Kaggle, and Google Dataset Search, with BERT achieving an accuracy of 99.61%.