Web Browser Anti-Phishing extension based On Website Trustworthiness Evaluation

Phishing attacks pose a significant threat to online users, necessitating the development of reliable real-time detection systems. To solve this problem, we propose a web browser anti-phishing extension designed to provide convenient and comprehensive protection. This study proposes machine learning techniques to assess a website's trustworthiness and provide warnings to users when they navigate to potentially dangerous sites. In this system, we use feature extraction engineering containing 36 features to extract the attributes or characteristics of the website URL. Random forest classifiers will be trained and tested on datasets preprocessed by this feature engineering, including legitimate URLs and phishing URLs, derived from the latest PhishTank and Google site rankings to ensure a representation of contemporary phishing tactics.