<![CDATA[This study examines the performance of a Support Vector Machine (SVM) model with a Radial Basis Function (RBF) kernel for phishing email detection using Euclidean and Manhattan distance measures. The dataset consists of 3,600 email samples, including 2,400 legitimate emails and 1,200 phishing instances. The features are designed to capture both linguistic and structural characteristics of emails, including word count, vocabulary diversity, stop word usage, number of links and domains, presence of email addresses, spelling errors, and urgency-related terms. The experiments were conducted using two train-test split ratios, 80:20 and 70:30, combined with hyperparameter tuning of C and gamma across 15 iterations. The findings indicate that the Manhattan distance consistently outperforms the Euclidean distance, particularly in terms of recall and F1-score, which are critical for detecting the minority class. The model achieved a best accuracy of 78.33%, accompanied by noticeable improvements in recall and F1-score. These results suggest that the choice of distance function within the RBF kernel plays a crucial role in enhancing model sensitivity and generalization when dealing with imbalanced data. Furthermore, the iterative hyperparameter tuning process contributes significantly to improving both performance and model stability. Overall, the SVM-RBF approach with Manhattan distance provides an effective and reliable framework for phishing email detection in machine learning applications.]]>
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