PHISHING DETECTION SYSTEM USING ENSEMBLED METHODS

This study proposes a robust content- grounded system exercising machine learning models for detecting phishing websites and distinguishing them from safe bones using Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest, Adaboost, Nearest Neighbor, and Neural Network algorithms, the system aims to directly identify fraudulent websites. By assaying features similar as URL, HTML content, and runner structure, the models are trained to fete patterns reflective of phishing attempts. Through expansive trial and evaluation, this exploration demonstrates the effectiveness of employing different machine literacy ways in combating cyber pitfalls. The proposed approach offers a promising result for enhancing online security measures, furnishing druggies and associations with a dependable tool to identify and alleviate the pitfalls associated with vicious websites, therefore securing sensitive information and conserving digital trust. Important