https://github.com/sayande01/phising_url_detection_randomforest
This project builds a robust binary classification system to detect phishing websites using URL features like SSL state, web traffic, and domain patterns. Achieving 97% accuracy, 96.8% precision, and 0.9953 ROC-AUC, it ensures reliable, efficient detection with interpretable insights.
https://github.com/sayande01/phising_url_detection_randomforest
binary-classification logistic-regression random-forest-classifier roc-analysis svc
Last synced: 2 months ago
JSON representation
This project builds a robust binary classification system to detect phishing websites using URL features like SSL state, web traffic, and domain patterns. Achieving 97% accuracy, 96.8% precision, and 0.9953 ROC-AUC, it ensures reliable, efficient detection with interpretable insights.
- Host: GitHub
- URL: https://github.com/sayande01/phising_url_detection_randomforest
- Owner: sayande01
- Created: 2024-11-15T12:49:44.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-15T12:53:53.000Z (7 months ago)
- Last Synced: 2025-02-13T02:38:37.794Z (4 months ago)
- Topics: binary-classification, logistic-regression, random-forest-classifier, roc-analysis, svc
- Language: Jupyter Notebook
- Homepage:
- Size: 2.81 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0