WEB BASED FAKE URL BLOCKER SYSTEM

Abstract

The rapid growth of internet services and online transactions has significantly increased the number of cyber-attacks, particularly phishing attacks that exploit fake or malicious URLs. Attackers frequently create deceptive websites that resemble legitimate platforms in order to steal sensitive information such as login credentials, banking details, and personal data. Traditional security mechanisms mainly rely on blacklist-based approaches, which are often ineffective against newly generated phishing websites. This research proposes a web-based fake URL blocker system that utilizes machine learning techniques to detect and prevent malicious URLs in real time. The system analyzes multiple characteristics of a URL, including lexical features, domain information, and structural patterns, to determine whether the URL is legitimate or suspicious. A machine learning classifier is used to evaluate these features and automatically block malicious websites before users can access them. The proposed architecture consists of a user interface module, feature extraction component, machine learning classification engine, and decision module. Experimental evaluation shows that the proposed system can effectively detect phishing URLs with high accuracy and reduced false detection rates. The system can be integrated with web browsers and enterprise security solutions to enhance overall online safety.