NoPhish：采用机器学习技术的高效钓鱼检测Chrome扩展程序
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
AI总结：
本研究开发了名为NoPhish的Chrome钓鱼检测扩展程序，基于PhishTank数据集与Alexa的22项特征，用多种机器学习算法检测钓鱼网页，实验表明随机森林算法精确率最优。
摘要：
通过网页浏览器提供的数字化服务的发展简化了我们日常的商务事务流程。但与此同时，这也让网页浏览器成为多种网络攻击的重点目标。网络钓鱼是一种众所周知的网络攻击，攻击者伪装成可信赖的网络服务器，以获取用户的敏感信息，例如信用卡号、银行信息、个人身份证号、社会安全号码以及用户名和密码。近年来，人们开发了许多技术来识别用户访问的真实网页，并在网页为钓鱼页面时向用户发出警告。本文中，我们为最受欢迎的网页浏览器Chrome开发了一款扩展程序，它将充当用户与钓鱼网站之间的中间件。这款名为“NoPhish”的Chrome扩展程序将基于多种机器学习技术识别钓鱼网页。我们使用了来自“PhishTank（钓鱼坦克）”的训练数据集，并提取了经Alexa数据库评定的22个最常用特征。所使用的训练算法包括Random Forest（随机森林）、Support Vector Machine（支持向量机）和k-Nearest Neighbor（k近邻）。性能结果显示，Random Forest的精确率最高。
英文摘要：
The growth of digitalization services via web browsers has simplified our daily routine of doing business. But at the same time, it has made the web browser very attractive for several cyber-attacks. Web phishing is a well-known cyberattack that is used by attackers camouflaging as trustworthy web servers to obtain sensitive user information such as credit card numbers, bank information, personal ID, social security number, and username and passwords. In recent years many techniques have been developed to identify the authentic web pages that users visit and warn them when the webpage is phishing. In this paper, we have developed an extension for Chrome the most favorite web browser, that will serve as a middleware between the user and phishing websites. The Chrome extension named "NoPhish" shall identify a phishing webpage based on several Machine Learning techniques. We have used the training dataset from "PhishTank" and extracted the 22 most popular features as rated by the Alexa database. The training algorithms used are Random Forest, Support Vector Machine, and k-Nearest Neighbor. The performance results show that Random Forest delivers the best precision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
