Data Sampling Optimization for Improved Classification of Imbalanced Phishing Datasets

Phishing is a social engineering attack that captures information by impersonating trusted entities. To detect it, researchers use Machine Learning as a classification task. However, phishing datasets are often imbalanced due to Concept Drift and the semantic nature of attacks. Oversampling, undersampling, and hybrid techniques address this, with hybrids combining both strategies for better results. This study examines the impact of optimizationbased sequencing of sampling algorithms on phishing data and compares it to traditional methods. Results show that optimized sequences improve classifier performance and reduce the effects of imbalance.