The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mock-up websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. In this paper, we compared the results of multiple machine learning methods for predicting phishing websites.

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使用机器学习技术进行网络钓鱼检测

互联网已经成为我们生活中不可或缺的一部分，然而，它也提供了匿名执行网络钓鱼等恶意活动的机会。网络钓鱼者试图通过社会工程或创建模拟网站来欺骗受害者，以窃取个人和组织的帐户 ID、用户名、密码等信息。虽然已经提出了许多方法来检测网络钓鱼网站，但网络钓鱼者已经改进了他们的方法来逃避这些检测方法。检测这些恶意活动的最成功方法之一是机器学习。这是因为大多数网络钓鱼攻击都有一些可以通过机器学习方法识别的共同特征。在本文中，我们比较了多种机器学习方法用于预测网络钓鱼网站的结果。