Enhancing Phishing URL Detection Using a Two-Level Rule-Based Framework Combining Lexical and RDAP Registration Features

Wan Afifie Aliff Bin Wan Abdullah and Zulkiflee Muslim and Haniza Nahar and Radzi Motsidi

International Journal of Research and Innovation in Social Science , Volume 10 , Issue 8 , pp. pp. 330-347 | 2026 | ISSN: 2454-6186

Abstract

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.