| language: en | |
| license: apache-2.0 | |
| tags: | |
| - xgboost | |
| - machine-learning | |
| - classification | |
| - cybersecurity | |
| - phishing-detection | |
| datasets: | |
| - custom | |
| metrics: | |
| - accuracy | |
| - precision | |
| - recall | |
| - f1 | |
| # XGBoost Phishing Detection Models | |
| ## Model Description | |
| XGBoost models trained for phishing detection using URL and HTML content features. | |
| This model is trained using XGBoost for binary classification tasks. | |
| ## Model Architecture | |
| - **Model Type**: XGBoost Classifier | |
| - **Framework**: XGBoost | |
| - **Task**: Binary Classification | |
| ## Usage | |
| ```python | |
| import joblib | |
| from huggingface_hub import hf_hub_download | |
| # Download the model | |
| model_path = hf_hub_download(repo_id="th1enq/xgboost_checkpoint", filename="xgboost phishing detection models.joblib") | |
| # Load the model | |
| model = joblib.load(model_path) | |
| # Make predictions | |
| predictions = model.predict(X_test) | |
| ``` | |
| ## Training | |
| The model was trained using the XGBoost library with the following approach: | |
| - Feature extraction from URLs/HTML content | |
| - Binary classification (legitimate vs phishing) | |
| - Cross-validation for model evaluation | |
| ## Files | |
| - `xgboost phishing detection models.joblib`: The trained XGBoost model | |
| - `features.py`: Feature extraction functions | |
| - `URLFeatureExtraction.py`: URL-specific feature extraction | |
| ## License | |
| This model is released under the Apache 2.0 License. | |