1 Mrs.G.PRIYANKA,2 VANGALA BHANU , 3 BASU NAVEEN KUMAR , 4 DANDUGULA PRASHANTH , 5 JUKANTI SATHVIKA 03 04 Phishing is a type of cyberattack in which attackers design fraudulent websites that closely imitate legitimate platforms to deceive users into revealing sensitive information such as login credentials banking details or personal data Recent studies highlight the effectiveness of machine learning ML techniques in detecting phishing websites by analyzing features such as URL structures webpage content and domainrelated information Although these methods achieve high accuracy they often lack realtime protection and seamless integration into userfriendly applications To overcome these limitations this project proposes a browser extension powered by a machine learning model that can detect phishing attempts during web browsing The system extracts important features from web pages and processes them through a lightweight yet efficient ML classifier which provides instant alerts to users when a potential threat is identified The model is trained on a balanced dataset containing both legitimate and phishing websites to ensure reliable and consistent performance By combining realtime detection with an easytouse interface the proposed solution enhances user safety and provides an effective defense mechanism against phishing attacks in everyday internet usage Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026