Paper Key : IRJ************651
Author: Ahmed Faraz Z ,Sarala Devi V
Date Published: 07 Apr 2024
Abstract
ABSTRACT:The increasing sophistication of phishing attacks poses a significant threat to online security, targeting unsuspecting users through deceptive URLs designed to mimic legitimate websites. This research presents a novel approach to phishing URL detection using machine learning techniques integrated into a web application. By analyzing various features such as URL structure, domain information, and website content, a machine learning model is trained to distinguish between legitimate and phishing URLs with high accuracy. The developed web application offers real-time URL scanning functionality, providing users with immediate phishing detection results to enhance their online safety.I.INTRODUCTION:The online email and online payment industry has been victimized by phishing more than any other industry. Phishing can be done through email phishing and phishing, so users should be aware of the consequences and should not trust a generic security application percent. Machine learning is one of the most powerful phishing detection techniques because it eliminates the shortcomings of the existing approach. The goal, which is the most important thing of the proposed system, is to verify the authenticity of the website by capturing blacklisted URLs. Notifies the user on the blacklisted site with popups when they try to access, and notifies the blacklisted user via email when they try to access. This proposed project allows an administrator to list URLs to alert the user during a request 1.This article finds that a higher degree of accuracy can be achieved using different features from previous studies. Unlike previous studies, a new study was conducted based on selected and coded characteristics of more characteristics. Properties were determined by URL analysis. A machine learning method was used to compare accuracy levels of different algorithms and model training times. We are trying to implement a phishing detection system by analyzing the web page URL. A URL is a complex string that syntactically and semantically expresses expressions for resources available on the Internet 2.
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