使用集成学习的网络钓鱼网站检测

Q2 Engineering
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引用次数: 0

摘要

网络钓鱼也是最常见的数据泄露类型。因此,它是通过发送带有指向欺诈网站链接的电子邮件来实施的。这种技术特别针对大公司。通常,攻击者会发送带有工作信息的电子邮件。机器学习是检测网络钓鱼最成功的技术之一。本文分析了各种机器学习技术预测网络钓鱼网站的结果。并介绍了用于识别网络钓鱼网站的各种方法。其中包括SVM分类方法、随机森林方法和AdaBoost方法。集成模型结合了支持向量机、随机森林和AdaBoost方法,能够以96%的准确率对钓鱼网站进行分类
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phishing Website Detection Using Ensemble Learning
Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%
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