Volume 2 - Issue 2, March - April 2026

The rapid expansion of internet usage has significantly increased the number of malicious websites and phishing attacks that target unsuspecting users. Traditional security mechanisms such as blacklist-based URL filtering are often ineffective against newly generated malicious links. This paper proposes an Artificial Intelligence (AI)-based URL Threat Detector designed to identify and classify potentially harmful URLs in real time. The system analyzes multiple URL-based features including lexical characteristics, domain information, and structural patterns to determine whether a link is safe or malicious. Machine learning algorithms are used to train the detection model using previously labeled datasets of benign and malicious URLs. The proposed system is implemented as a web application that allows users to submit URLs for instant threat analysis. The model predicts the threat level and provides users with immediate security feedback. Experimental results demonstrate that the AI-based approach improves detection accuracy, reduces response time, and provides better protection compared to traditional blacklist-based detection systems. The proposed solution contributes to improved cybersecurity by enabling automated, scalable, and intelligent detection of malicious web links.

Dr.K.Dhivya, G.Sharon,"AI- Based URL Thread Detector" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(900-905) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.