Rasel, Annajiat AlimRinvee, Tajbiha MehonajSayara, NowshinBhuiyan, Ziana Jesin2025-09-162025-09-1620252025-04ID 21301311ID 21301149ID 21301324http://hdl.handle.net/10361/26752Cataloged from PDF version of thesis.Includes bibliographical references (pages 33-34).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.This paper analyzes the integration of artificial intelligence (AI) into security to impede the continual risk of malicious links. In a dynamic digital ecosystem, links create an ongoing challenge to user security. In this paper, we propose developing and implementing machine learning (ML) models that enable real-time detection and suppression of malicious links. Through training on diverse datasets of malicious links, the AI system can evolve strategies. Rigorous testing ensures the efficiency of the integrated protection. The outcome is to redefine security by amplifying an AI-driven solution, offering a proactive firewall against spam links, and improving overall digital safety for consumers.35 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Machine learningArtificial intelligenceDigital ecosystemCybersecurityDigital safetyIntegrated protectionWeb securityURL filteringNetwork securityComputer networks--Security measures.Computer security.Computational intelligence.Anomaly detection (Computer security).Thesis