Phishing Websites Dataset For Security Classification

Phishing websites data is a collection of features used to distinguish legitimate and fraudulent web pages. The dataset is hosted on the UCI Machine Learning Repository, a known source for benchmark datasets. The original creator and specific collection date are not provided.

Use Cases

• Train a binary classifier to predict phishing status using features like URL length, presence of IP address, and SSL certificate details.
• Analyze feature importance to identify which URL, domain, or page content attributes are most indicative of a phishing attempt.
• Build a rule-based detection system by examining thresholds for numerical features such as the number of redirects or abnormal URL structures.

Strengths

• Dataset is hosted on the UCI repository, which provides a standard format and documentation for machine learning tasks.
• It is a canonical benchmark dataset for evaluating phishing detection algorithms.

Limitations

• The exact number of rows and features is unknown, making it difficult to assess scale and complexity.
• The data's age and update frequency are not specified, which may affect model relevance for modern phishing techniques.

Provenance

Source

UCI Machine Learning Repository