Phishing and Legitimate Emails for Machine Learning Training

A collection of emails labeled as phishing or legitimate, intended for training machine learning models. The dataset's origin, size, and specific features are not detailed in the provided metadata. Further inspection of the downloaded files is required to determine its full scope and composition.

Use Cases

• Train binary classifiers to distinguish phishing from legitimate emails based on email content.
• Benchmark phishing detection algorithms using a labeled corpus of emails.
• Analyze linguistic and structural patterns common in phishing attempts based on the email text.

Strengths

• Contains labeled data for a specific, high-impact security task.
• Designed explicitly for training machine learning models.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle