DETECTING HARMFUL WEB LINKS USING INTELLIGENT MACHINE LEARNING MODELS

Abstract

The rapid expansion of the internet has led to an increase in malicious web links. Traditional blacklist-based detection methods are often insufficient, as attackers continuously generate new, previously unseen harmful URLs. This project presents an intelligent machine learning–based system for detecting harmful web links with high accuracy and real time efficiency. Various machine learning algorithms—including Logistic Regression, Random Forest—are trained and evaluated to determine the most effective approach. Overall, the project demonstrates the potential of intelligent ML-based models to strengthen web security and safeguard digital environments.