The increase in the number of phishing demands innovative solutions to safeguard users from phishing attacks. This study explores the development and utilization of a real-time browser extension integrated with machine learning model to improve the detection of phishing websites. The results showed that the model had an accuracy of 98.32%, precision of 98.62%, recall of 97.86%, and an F1-score of 98.24%. When compared to other algorithms like Support Vector Machine, Na\"ive Bayes, Decision Tree, XGBoost, and K Nearest Neighbor, the Random Forest algorithm stood out for its effectiveness in detecting phishing attacks. The zero-day phishing attack detection testing over a 15-day period revealed the model's capability to identify previously unseen threats and thus achieving an overall accuracy rate of 99.11%. Furthermore, the model showed better performance when compared to conventional security measures like Google Safe Browsing. The model had successfully detected phishing URLs that evaded detection by Google safe browsing. This research shows how using machine learning in real-time browser extensions can defend against phishing attacks. It gives useful information about cybersecurity and helps make the internet safer for everyone.

翻译：网络钓鱼攻击数量的增长要求创新的解决方案来保护用户免受其害。本研究探讨了集成机器学习模型的实时浏览器扩展程序的开发与应用，以提升对钓鱼网站的检测能力。结果表明，该模型的准确率达到98.32%，精确率为98.62%，召回率为97.86%，F1分数为98.24%。与支持向量机、朴素贝叶斯、决策树、XGBoost和K近邻等其他算法相比，随机森林算法在检测钓鱼攻击方面表现出卓越的有效性。为期15天的零日钓鱼攻击检测测试表明，该模型能够识别先前未见的威胁，总体准确率达到99.11%。此外，与Google安全浏览等传统安全措施相比，该模型展现出更优的性能，成功检测出规避了Google安全浏览检测的钓鱼URL。本研究证明了在实时浏览器扩展中应用机器学习技术可有效防御网络钓鱼攻击，为网络安全领域提供了实用见解，有助于为所有人构建更安全的互联网环境。