Supervised URLs Identification Dataset (SupURLsIdDs)

The Supervised URLs Identification Dataset (SupURLsIdDS) is a comprehensive, labeled dataset designed for developing and benchmarking robust machine learning models dedicated to intelligent and scalable malicious Uniform Resource Locator (URL) detection.This novel, multi-class repository contains 651,191 URLs and their corresponding threat labels across four distinct categories: Benign, Phishing, Malware, and Defacement. The data was curated and aggregated from multiple public threat intelligence and research sources, including ISCX-URL-2016, PhishTank, and the Malware Domain Blacklist.The dataset is engineered for supervised learning and features fifteen distinctive lexical and structural featuresextracted directly from the URLs, such as token counts, special character frequency, and length metrics. These features are pre-processed and ready for direct input into classification models (e.g., Decision Trees and Random Forest). SupURLsIdDS is intended to serve as a high-quality benchmark for researchers developing real-time, resource-efficient, and accurate web security applications