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First is the data collection and preprocessing phase.
This research uses two publicly available datasets from Mendeley, referred to as D1 and D2. These datasets contain labeled examples of both legitimate and phishing websites. Dataset D1 includes 10,000 balanced entries, half legitimate and half phishing, with 48 features each. Dataset D2 is much larger, containing 235,795 samples with a slightly imbalanced split, which consists of 57.2% legitimate and 42.8% phishing, and 54 features. Together, these datasets provide a diverse and up-to-date representation of modern web-based threats.
To ensure rigorous evaluation, each dataset was partitioned using stratified random sampling with a preset random state (313) into three subsets: 70% for training, 15% for validation, and 15% for final testing. This approach maintains the original class distributions across all splits while guaranteeing reproducible results. Categorical features were processed using one-hot encoding to ensure compatibility with machine learning algorithms. |
IEEE/ICACT20260061 Slide.10
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