Phishing and Legitimate URL Features for Machine Learning

A large-scale dataset of phishing and legitimate URLs with engineered features for machine learning. The dataset is sourced from Kaggle, but the specific author, organization, and creation date are unknown. It is designed for binary classification tasks in cybersecurity.

Use Cases

• Train binary classification models to distinguish phishing URLs from legitimate ones based on engineered features.
• Benchmark feature engineering techniques for cybersecurity applications.
• Analyze the characteristics of malicious URLs for threat intelligence.
• Develop and test real-time phishing detection systems.

Strengths

• Dataset is described as 'large-scale', suggesting a substantial number of samples.
• Includes engineered features specifically for machine learning analysis.

Limitations

• Row count, column names, and sample data are unavailable, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaggle