Phishing-Attack-Domain-Detection and Phishing-Website-Detection

Both projects are independent machine learning models for phishing URL detection, making them competitors in the "phishing-url-detection" category.

Maintenance 0/25
Adoption 8/25
Maturity 16/25
Community 20/25
Maintenance 0/25
Adoption 7/25
Maturity 8/25
Community 17/25
Stars: 56
Forks: 23
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: GPL-2.0
Stars: 30
Forks: 8
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License:
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About Phishing-Attack-Domain-Detection

deepeshdm/Phishing-Attack-Domain-Detection

Identifying Malicious Phishing URLs through Machine Learning

This tool helps individuals and organizations detect malicious phishing URLs before they can cause harm. You input a website address, and it tells you whether the URL is legitimate or a fraudulent phishing attempt. It is designed for anyone who needs to verify the safety of a link, from cybersecurity professionals to everyday internet users.

cybersecurity fraud-prevention website-safety link-verification internet-security

About Phishing-Website-Detection

gangeshbaskerr/Phishing-Website-Detection

A project that predicts a phishing URL by extracting 17 features in 3 different categories and then train and test the machine learning models using a dataset from Phishtank.

This tool helps cybersecurity professionals and system administrators detect phishing websites. You input a URL, and it classifies whether the URL is legitimate or a phishing attempt. The system analyzes various features of the URL, its address bar, domain, HTML, and Javascript to make an informed decision.

cybersecurity phishing-detection url-analysis threat-intelligence online-security

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