| --- |
| language: |
| - az |
| - en |
| license: mit |
| tags: |
| - cybersecurity |
| - phishing-detection |
| - url-classification |
| - threat-intelligence |
| metrics: |
| - precision |
| - recall |
| - roc_auc |
| - pr_auc |
| - mcc |
| - brier_score |
| model-index: |
| - name: urlaz |
| results: |
| - task: |
| type: url-classification |
| name: Phishing URL Detection |
| metrics: |
| - type: precision |
| value: 0.9980 |
| - type: recall |
| value: 0.9550 |
| - type: pr_auc |
| value: 0.9983 |
| - type: roc_auc |
| value: 0.9989 |
| --- |
| |
| # 🛡️ URLAZ — Phishing URL Detection Engine |
|
|
| **URLAZ** is a lightweight, high-performance Machine Learning model built for real-time URL-based phishing detection. |
|
|
| --- |
|
|
| ## 📊 Benchmark Metrics (5-Fold GroupKFold Cross-Validation) |
|
|
| | Metric | Score | Description | |
| |---|---|---| |
| | **Precision** | **99.80%** | Test set precision at operational threshold | |
| | **Recall** | **95.50%** | Phishing detection recall | |
| | **PR-AUC** | **0.9983** | Precision-Recall Area Under Curve | |
| | **ROC-AUC** | **0.9989** | Receiver Operating Characteristic AUC | |
| | **MCC** | **0.9781** | Matthews Correlation Coefficient | |
| | **Brier Score** | **0.0078** | Probability Calibration Score | |
|
|
| --- |
|
|
| ## 💻 Quickstart (Python) |
|
|
| ### 1. Download Model from HuggingFace |
| ```python |
| from huggingface_hub import hf_hub_download |
| import joblib |
| |
| # Download model weights |
| model_path = hf_hub_download(repo_id="alixansec/urlaz", filename="urlaz_phishing_detector.joblib") |
| model = joblib.load(model_path) |
| ``` |
|
|
| ### 2. Predict URL |
| ```python |
| # Pass 35 structural features extracted from URL (see predict_url.py) |
| probability = model.predict_proba([features])[0][1] |
| |
| if probability >= 0.95: |
| print("🔴 PHISHING DETECTED") |
| else: |
| print("🟢 SAFE") |
| ``` |
|
|
| --- |
|
|
| ## 📁 Repository Contents |
|
|
| - `urlaz_phishing_detector.joblib` — Serialized binary classifier |
| - `urlaz_phishing_detector.sha256` — SHA-256 integrity signature |
| - `phishing_urls_verified.txt` — Verified targeted phishing dataset (3,067 records) |
| - `predict_url.py` — Inference prediction script |
|
|
| --- |
|
|
| ## 🔒 Integrity Signature |
| - **SHA-256:** `c6a21e5a06901d6f3ba848a2d6c8507ff48c2cb52b2d8d80da6717a142c0a445` |
|
|
