Enhancing Phishing URL Detection Using a Two-Level Rule-Based Framework Combining Lexical and RDAP Registration Features
Phishing remains one of the most persistent cyber threats, and almost every campaign ultimately depends on a deceptive Uniform Resource Locator (URL). Existing defences face a structural trade-off: blacklists are reactive and cannot cover newly registered domains during the zero-hour window, while machine-learning detectors, although accurate, are opaque, feature-hungry, and often depend on page content or full DNS telemetry that many organisations cannot collect. This study proposes and evaluates a lightweight, fully interpretable two-level rule-based framework that fuses lexical URL features with domain registration evidence retrieved through the Registration Data Access Protocol (RDAP). Level 1 scores each URL using five transparent lexical rules derived from training-set distributions of domain length, number of dots, number of hyphens, number of digits, and URL entropy. Level 2 applies three RDAP rules covering domain age, days to expiry, and a missing-registration-data flag, targeting the young, short-lived, and poorly documented domains that characterise phishing infrastructure. The two levels are combined through logical OR and AND decision fusion and evaluated on a balanced, held-out set of 400 URLs drawn from a curated corpus of 800. Level 1 achieved 95.50% accuracy (precision 0.9789, recall 0.9300); Level 2 achieved perfect recall (1.0000) at 0.8969 precision; OR fusion preserved perfect recall; and AND fusion delivered the best overall result at 96.50% accuracy with perfect precision, zero false positives, and a Matthews Correlation Coefficient of 0.9323. A confusion-matrix decomposition further shows that the false-positive sets of the two levels are completely disjoint, confirming that lexical and registration evidence fail independently. Exploiting this, a cascaded implementation of AND fusion reproduces identical decisions while issuing RDAP queries for only 47.5% of URLs, a 52.5% reduction in external lookups.