--- 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`