metadata
language: en
license: other
tags:
- security
- phishing-detection
- url-classification
- xgboost
Random Forest / XGBoost Model for URL Phishing Detection
Model Details
- Architecture: Gradient-boosted decision trees (XGBoost)
- Input: Single URL string (no external queries)
- Features: Lexical and structural URL features (lengths, symbol counts, digit ratio, IPv4 pattern, common phishing tokens, scheme/TLD heuristics)
- Training data:
PhiUSIIL_Phishing_URL_Dataset.csv - Intended use: Binary classification (phishing vs. legitimate)
Metrics (test)
- Accuracy: 0.9952
- Precision: 0.9928
- Recall: 0.9989
- F1: 0.9958
- ROC-AUC: 0.9976
Usage
See README.md and inference.py for loading and predict_url().
Limitations and Biases
- URL-only features can be evaded by sophisticated attackers.
- Dataset shifts and novel TLDs may degrade performance.
- Always validate on your own traffic before deployment.
License
Provided for research/educational purposes. Ensure compliance with local laws and organizational policies.