AI Based Web Approach System for Detecting Malicious URLs and Preventing Cyber Fraud

Abstract: Describehowadvancesindeeplearning,especially As the internet becomes more essential in our daily lives, cyber fraud— especially through harmful links—has become a serious issue. This project introduces a smart, web-based system that detects and classifies dangerous URLs in real time, such as phishing, malware, or defacement links. It uses machine learning models trained on a mix of safe and harmful URLs by analyzing features like link structure, special characters, and keywords. Built using Flask, the system provides a simple interface where users can check URLs. It also includes a feedback option, so users can help improve accuracy by confirming or correcting results, which helps the system learn and improve over time. The system can also scrape live webpage content and display the main text in a clean, readable format to help users understand what the page is about. A whitelist of trusted domains helps avoid unnecessary checks on safe websites. The design is light, fast, and easy to expand in the future with features like scanning multiple links, analyzing content tone, or auto-flagging suspicious content. Overall, this system offers a smart, user-friendly, and effective way to fight cyber threats using AI and real-time analysis.