Real time identification of phishing attacks through machine learning enhanced browser extensions

Phishing attack continues to rank among the deadliest online threats. They create phony websites in an attempt to obtain personal data. This study offers a framework for a browser extension that uses machine learning to examine URLs and visual components in Google Chrome in order to identify phishing websites in real-time. Using support vector machine (SVM), decision tree (DT), and random forest (RF) algorithms, the suggested system gathers and examines data from websites, extracts hybrid elements including lexical, structural, and visual layout parameters, and arranges them. The best traits that can distinguish between items are found using the grey wolf optimizer (GWO). This reduces computer power consumption and facilitates finding items. GWO enhanced the random forest model, which performed well on benchmark datasets such as the Berkeley ML Archives and PhishTank. On the MCC test, it received a score of 0.96 and had an accuracy rate of 98.7%.This method is used by the Chrome extension to assess URLs for visual similarity in real time and display warnings to users that change according to their actions.The proposed system is better than current anti-phishing solutions because it works better in real time, has a lower false-positive rate, and can handle obfuscated URLs. This project makes a useful, user-centered defense system that can protect against phishing attacks that change over time by using smart security at the browser level.