Smart Web Link Threat Detection using Machine Learning Techniques

Abstract

The rapid growth of the internet has led
to a surge in malicious web links that
spread malware, phishing attacks, and
fraudulent content. Traditional security
systems often fail to detect harmful URLs
in real time due to the sophistication of
modern cyber threats. This project
proposes an intelligent machine learningbased
system for detecting harmful web
links efficiently. The system extracts
features from URLs such as length,
domain, character patterns, and content
attributes. Supervised learning
algorithms, including Random Forest,
Support Vector Machines, and Neural
Networks, are used for classification.
Feature engineering improves detection
accuracy. The system also uses blacklist
databases for comparison. Real-time
detection ensures protection against
phishing and malware threats. Adaptive
learning updates the model with new
patterns of harmful links.