Web Phishing Detection using List based, Learning based and Heuristic based Detection Technique

The rise of modern technology and the increasing reliance on digital communications have made phishing a critical cybersecurity threat. Attackers use sophisticated techniques to manipulate users and gain unauthorized access to sensitive information. These attacks often leverage social engineering and deceptive websites to steal data and download malware, leading to serious consequences such as data breaches, financial fraud, and ransomware infections. To counter this, we propose a comprehensive phishing detection system that analyses phishing websites embedded in emails and URLs. The system integrates list-based, learning-based, and heuristic detection techniques to improve accuracy and adaptability. Additionally, decision-structure algorithms categorize websites according to structural and behavioural functions. Experimental evaluation using a real-world dataset demonstrates a high detection accuracy of 97%, highlighting the effectiveness of the proposed system in improving cybersecurity defenses.