PHISHNET: Multi-Algorithmic Safety Net for Advanced Phishing URL Detection

Phishing remains one of the simplest yet most dangerous techniques used by cybercriminals to steal confidential data from unsuspecting users. Attackers attempt to deceive individuals into disclosing vital details such as logon credentials, bank information, or personal identification numbers. Cybersecurity experts continue to develop dependable systems to accurately differentiate between legitimate and fraudulent websites. This study applies machine learning models to examine various URL characteristics in order to detect phishing attempts. Models Like ‘The Support Vector Machine’ (SVM), ‘The Decision Tree’, and ‘The Random Forest’ are trained and compared based on performance indicators including validity, false +ve rate, and false negative rate. The main goal of this research is to acquire the most effectual algorithm for phishing URL detection through detailed comparative analysis.