An Interpretable Exploratory Framework for Analyzing Structural Characteristics of Phishing Websites
Keywords:
phishing website detection, lexical URL analysis, exploratory data analysis, explainable cybersecurity, phishing behavior analysisAbstract
Phishing attacks continue to be a persistent danger to cybersecurity by exploiting technical flaws and human trust. Previous studies have primarily focused on machine learning performance, whereas relatively few have examined the structural characteristics that differentiate phishing websites from legitimate websites. In this paper, we provide an interpretable exploratory approach to analyze phishing behavior utilizing structural, symbolic and domain related website features from a publicly available phishing dataset. The analytical approach incorporates distribution analysis, non-parametric statistical testing and correlation-based investigation to explore structural anomalies associated with phishing. The study analyzes a large-scale phishing website dataset containing both phishing and legitimate instances represented by URL-based and domain-related attributes. Representative features were selected through preprocessing and redundancy filtering to retain structurally informative indicators while improving analytical interpretability. The proposed framework emphasizes statistical understanding of phishing behavior rather than predictive optimization, enabling a transparent examination of feature distributions, class-specific differences, and feature interactions. The results indicate that phishing URLs display more structural variation, and more apparent right-skewed distributions, especially for URL length and symbolic composition patterns. Statistical tests validated significant structural differences between phishing and legitimate websites (p < 0.001). Correlation analysis showed a moderate positive link between URL length and symbolic manipulation behavior. Overall, the results show that the interpretability and the analytical robustness of phishing analysis are improved when multiple structural indicators are evaluated jointly rather than in isolation.
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