Enhancing the Effectiveness of Machine Learning-Based Phishing Email Detection via an Improved Pre-Processing Technique for Data Security

Abstract

The growing volume and sophistication of phishing emails have become a significant threat to data security, often serving as the initial vector for data breaches. Most past studies have focused on comparing machine learning models to determine the best-performing algorithm. They often neglect the role of pre-processing, which also contributes to the effectiveness of these models. To address this gap, this study investigates the impact of pre-processing techniques on phishing email detection, aiming to strengthen data protection. Three supervised machine learning algorithms, which are Support Vector Machine (SVM), Random Forest and Decision Tree, were selected to undergo two experimental iterations: one with basic pre-processing and the other with an enhanced pre-processing technique including Synthetic Minority Oversampling Technique (SMOTE), Term Frequency-Inverse Document Frequency (TF-IDF), Singular Value Decomposition (SVD) and cross-validation. Using a dataset comprising 28,747 labelled emails, the models were trained, tested, and evaluated based on accuracy, precision, recall, and F1-score, with further insight gained through confusion matrix analysis. Among the models, Random Forest demonstrated the strongest consistent performance across all metrics, while Decision Tree showed the most notable improvement. Although SVM maintains high recall and precision, it is less responsive to the applied pre-processing techniques. This result demonstrates that pre-processing techniques significantly contribute to the performance of the detection models. Overall, these findings highlight the critical role of pre-processing in enhancing phishing email detection, which contributes to stronger organisational resilience.