Result Paper On Cyberfence: Intelligent Defence Against Phishing Links

Abstract

Phishing continues to be one of the most prevalent and damaging forms of cybercrime, exploiting social engineering techniques to deceive users into divulging sensitive information such as login credentials, banking details, and personal data. Traditional defenses such as spam filters, antivirus software, and employee awareness campaigns have proven insufficient against the growing sophistication of phishing attacks. This research proposes a machine learning–based phishing domain detection system that can identify malicious URLs before users interact with them, thereby minimizing the risks of compromise. The system analyzes domain-level and lexical features—such as URL length, numerical patterns, IP address usage, and entropy—to classify URLs as benign or phishing.