GitHub - MontaLabidi/PHISH-Detection-using-ML: Phish Rod is a web application that leverages machine learning to detect phishing websites.
![Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM | SpringerLink Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM | SpringerLink](https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs11277-021-08196-7/MediaObjects/11277_2021_8196_Fig2_HTML.png)
Smart Phishing Detection in Web Pages using Supervised Deep Learning Classification and Optimization Technique ADAM | SpringerLink
![GitHub - VaibhavBichave/Phishing-URL-Detection: Phishers use the websites which are visually and semantically similar to those real websites. So, we develop this website to come to know user whether the URL is phishing GitHub - VaibhavBichave/Phishing-URL-Detection: Phishers use the websites which are visually and semantically similar to those real websites. So, we develop this website to come to know user whether the URL is phishing](https://user-images.githubusercontent.com/79131292/144742785-d183f50a-52d6-4296-a43a-90a1ee3502d8.png)
GitHub - VaibhavBichave/Phishing-URL-Detection: Phishers use the websites which are visually and semantically similar to those real websites. So, we develop this website to come to know user whether the URL is phishing
![PDF] Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection | Semantic Scholar PDF] Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection | Semantic Scholar](https://d3i71xaburhd42.cloudfront.net/715be94df3590480a47dba3a6a1eb044e8814e79/5-Figure3-1.png)
PDF] Phishing Website Detection based on Supervised Machine Learning with Wrapper Features Selection | Semantic Scholar
![An effective detection approach for phishing websites using URL and HTML features | Scientific Reports An effective detection approach for phishing websites using URL and HTML features | Scientific Reports](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-022-10841-5/MediaObjects/41598_2022_10841_Fig1_HTML.png)
An effective detection approach for phishing websites using URL and HTML features | Scientific Reports
![Development of anti-phishing browser based on random forest and rule of extraction framework | Cybersecurity | Full Text Development of anti-phishing browser based on random forest and rule of extraction framework | Cybersecurity | Full Text](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42400-020-00059-1/MediaObjects/42400_2020_59_Fig3_HTML.png)