用机器学习检测钓鱼网站，为用户浏览器加装实时防护盾
NoPhish: Efficient Chrome Extension for Phishing Detection Using Machine Learning Techniques
- 基于随机森林等算法，分析网页特征识别钓鱼页面
- 在PhishTank数据集上，随机森林准确率达98.7%
- 专为Chrome设计，适合普通用户防诈骗
随着网络服务的普及，浏览器成为网络攻击的主要目标。网络钓鱼攻击通过伪装成可信网站，窃取用户信用卡号、银行信息、身份证号、社保号及账号密码等敏感数据。近年来已有多种技术用于识别真实网页并预警钓鱼页面。本文开发了名为NoPhish的Chrome浏览器扩展，作为用户与钓鱼网站间的中间层，利用机器学习技术识别钓鱼网页。采用PhishTank数据集，提取了由Alexa数据库评选出的22个最流行特征，使用随机森林、支持向量机和k近邻三种算法进行训练。实验结果表明，随机森林表现最佳，精度达到98.7%。
原文摘要 · Abstract (English)
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.
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