In this paper, an intelligent system to detect phishing attacks is presented. We used different data mining techniques to decide categories of websites: legitimate or phishing. Different classifiers were used in order to construct accurate intelligent system for phishing website detection.

This Colab notebook demonstrates how to build a phishing website detection model using the Random Forest algorithm. The model can be used to predict whether a given website is phishing or legitimate.

A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. The objective of this project is to train machine learning models...

Phishing URL Detection Using RandomForest. This project aims to detect phishing URLs using a RandomForest classifier. The process involves two main steps: feature extraction from URLs and prediction using a custom trained RandomForest model.

In this text, we have highlighted the principle functions which have verified to be dependable and powerful in predicting phishing websites. In addition, we delivered some new capabilities, assigned new rules to a few famous experiments, and up to date some different functions. DATA FLOW DIAGRAM USE CASE DIAGRAM

A web application model that uses Random Forest classifier to identify phishing URLs by extracting some of the features of the input URL.

Jul 6, 2018 · In this paper, we analyze web-based phishing detection by using Random Forest. Some important URL features are identified and our study shows that the detection performance with feature selection...

Our proposed method is novel and an extension to our previous work, where we identify phishing websites using a combined approach by constructing Resource Description Framework (RDF) models and using ensemble learning algorithms for the classification of websites. Our approach uses supervised learning techniques to train our system.

Jul 15, 2021 · Using Extreme Learning Machines, we proposed an intelligent model for detecting phishing web pages. There are different types of web pages with different features.

In this paper, we analyze web-based phishing detection by using Random Forest. Some important URL features are identified and our study shows that the detection performance with feature selection is improved.