A SMART ANTI-PHISHING MODEL FOR PHISHING WEBSITE DETECTION USING MACHINE LEARNING APPROACHES BASED ON HYBRID FEATURES

Phishing is a major concern in a changing society. The rise of the Internet has led to a new form of data theft, known as cybercrime. One of the most prevalent forms of cybercrime, phishing attempts to trick users into divulging personal information by creating a convincing look and feel of a trusted online service, like a bank, grocery store, or online media website. The problem of detecting phishing websites has been discussed on multiple platforms, with approaches varying from straightforward classifiers to complex hybrid systems. A novel phishing detection system, “Phishing URL Detection, PUD”, is proposed here. It uses machine learning approaches to analyze results from various methods applied to URLs and validates them against existing research. URL-based phishing is a prevalent method to collect user data when accessing a malicious website. Detecting rogue URLs is difficult. The proposed method seeks to discover such websites using machine-learning approaches that analyze the behavior and attributes of the suggested URL. To better understand the structure of malicious URLs, various machine-learning methods were tested for feature evaluation. Precise parameter tuning facilitates choosing the best machine learning method for identifying malicious from legitimate websites. One of the major goals is to train machine learning models to find and prevent phishing websites using the dataset. Various models' levels of performance are evaluated and contrasted. The proposed system outperforms state-of-the-art models and demonstrates the importance of hybrid URL features in phishing website detection.