In recent years, Cyber attacks have increased in number, and with them, the intensity of the attacks and their potential to damage the user have also increased significantly. In an ever-advancing world, users find it difficult to keep up with the latest developments in technology, which can leave them vulnerable to attacks. To avoid such situations we need tools to deter such attacks, for this machine learning models are among the best options. This paper presents a Browser Extension that uses machine learning models to enhance online security by integrating three crucial functionalities: Malicious URL detection, Spam Email detection and Network logs analysis. The proposed solution uses LGBM classifier for classification of Phishing websites, the model has been trained on a dataset with 87 features, this model achieved an accuracy of 96.5% with a precision of 96.8% and F1 score of 96.49%. The Model for Spam email detection uses Multinomial NB algorithm which has been trained on a dataset with over 5500 messages, this model achieved an accuracy of 97.09% with a precision of 100%. The results demonstrate the effectiveness of using machine learning models for cyber security.

翻译：近年来，网络攻击数量不断攀升，攻击强度及其对用户造成损害的潜在可能性也显著增加。在技术持续进步的世界中，用户难以跟上最新技术发展，这可能使其易受攻击。为避免此类情况，我们需要能够阻止此类攻击的工具，在这方面机器学习模型是最佳选择之一。本文提出一种浏览器扩展，通过集成恶意URL检测、垃圾邮件检测和网络日志分析三大关键功能，利用机器学习模型增强在线安全性。所提出的解决方案采用LGBM分类器对钓鱼网站进行分类，该模型在包含87个特征的数据集上进行训练，实现了96.5%的准确率、96.8%的精确率和96.49%的F1分数。用于垃圾邮件检测的模型采用Multinomial NB算法，该算法在包含5500余条消息的数据集上训练，实现了97.09%的准确率和100%的精确率。实验结果证明了机器学习模型在网络安全应用中的有效性。