|
|
| --- |
| license: mit |
| tags: |
| - phishing-detection |
| - url-classification |
| --- |
| |
| # Overview |
|
|
| The objective of the project is to classify if an URL is phishing or not. This model repo contains the required encoders (for url,dom and tld), scaler (for digit_cnt and is_https) and the trained model (RandomForest Classifier). |
|
|
| # Dataset & Attribution |
| This project uses the URL-Phish dataset. The dataset was obtained from Kaggle, where it is available as [Phishing URL Detection (111K URLs, 22 Features)](https://www.kaggle.com/datasets/sahandnamvar/phishing-url-detection-111k-urls-22-features). |
|
|
| The dataset is licensed under **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)**, |
| which permits sharing, redistribution, and adaptation with appropriate credit. |
|
|
| **Dataset citation**<br> |
| >Dam Minh, Linh; Tran Cong, Hung (2025).<br> |
| >URL-Phish: A Feature-Engineered Dataset for Phishing Detection.<br> |
| >Mendeley Data, V1.<br> |
| >DOI: https://doi.org/10.17632/65z9twcx3r.1<br> |
|
|
| **Original data sources referenced by the dataset authors**<br> |
| >PhishTank – Community-driven phishing URL repository<br> |
| >Research Organization Registry (ROR) dataset – Source of trusted benign domain URLs<br> |
|
|
| **Paper citation**<br> |
| >Dam Minh Linh, Tran Cong Hung, <br> |
| >A feature-engineered dataset of benign and phishing URLs for machine learning and large language models evaluation,<br> |
| >Data in Brief,<br> |
| >Volume 63,<br> |
| >2025,<br> |
| >112162,<br> |
| >ISSN 2352-3409,<br> |
| >https://doi.org/10.1016/j.dib.2025.112162. |
|
|
| **Modifications:** <br> |
| The following preprocessing was applied to the original dataset: |
| - Duplicate rows and null/missing values were checked for and removed, if present |
| - Feature scaling applied to selected numeric features |
| - TF-IDF encoding applied to selected URL/text-derived feature(s) |
| - Data split into train / validation / test sets |
|
|
| **Feature usage:** <br> |
| The final model was trained using a selected subset |
| of the features; the remaining |
| features were excluded at training time via feature selection, not by removing them from |
| the stored datasets. |
|
|
| # License |
| - **Code**: MIT License — see `LICENSE` |
| - **Data**: Raw and processed datasets are redistributed under **Creative Commons Attribution 4.0 International (CC BY 4.0) license**, consistent with the original dataset's license (see Dataset & Attribution above). |
| - **Model & preprocessors**: MIT License — trained artifacts are provided under the same |
| terms as the codebase. Model trained on **Creative Commons Attribution 4.0 International (CC BY 4.0) license** data; see Dataset & Attribution section for details. |
