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Phishing Detection Model Performance

Overview

This project develops a machine learning model for detecting phishing URLs using URLScan.io API data.

Model Performance Over Time

Performance Metrics

Track how the model's performance evolves over time to identify improvements or degradation.

Confusion Matrix

Latest model confusion matrix showing prediction accuracy across classes.

Feature Drift Detection

Monitor feature drift to detect changes in data distribution that may affect model performance.

Dataset Information

• Source: URLScan.io API + Phishing URL datasets
• Training samples: TBD
• Test samples: TBD
• Features: TBD

Model Architecture

Details about the model architecture, feature engineering, and training process will be documented here.

Latest Updates

Check back for updates on model performance and improvements.

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