Phishing URL Dataset Optimized for Machine Learning Detection

A curated collection of URLs designed for training and evaluating machine learning models to detect phishing attempts. The dataset is hosted on Kaggle, but specific details about its size, origin, and creation date are not provided. Its primary purpose is to serve as a benchmark for building and testing automated phishing detection pipelines.

Use Cases

• Train supervised classification models to distinguish phishing URLs from benign ones based on curated features.
• Benchmark the performance of different machine learning algorithms for cybersecurity threat detection.
• Develop feature engineering pipelines for extracting security-relevant signals from URL structures.
• Create educational or demonstration tools for phishing awareness based on labeled data.

Strengths

• Dataset is explicitly curated and optimized for machine learning detection pipelines, suggesting a focus on model-ready data.
• The description indicates a specific application domain (phishing detection), providing clear context for the data's utility.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment for large-scale training.
• Column-level documentation is absent; field semantics and feature engineering must be inferred after download.
• Last update date is unknown; freshness of the phishing examples is unverified.

Provenance

Source

Kaggle

Collection Method

Curated collection; specific gathering method is unknown.