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alixansec
/
urlaz

Joblib
Azerbaijani
English
cybersecurity
phishing-detection
url-classification
threat-intelligence
Eval Results (legacy)
Model card Files
xet
 Community
urlaz
File size: 2,133 Bytes
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---
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`