基于内容的网络钓鱼检测与机器学习

Uğur Ozker, O. K. Sahingoz
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引用次数: 8

摘要

近年来,由于互联网技术的必然发展,几乎所有现实世界的系统都转移到数字平台上。这增加了网络空间在我们生活的各个方面的使用,特别是移动设备,使我们能够随时随地连接到相关的服务。然而,这种不可避免的扩展也带来了许多安全漏洞,特别是对于标准终端用户。网络钓鱼是黑客最喜欢使用的攻击类型之一,因为它很容易阻碍自己。这种类型的攻击最初是通过简单的电子邮件或社交媒体消息触发的,主要是将受害者转发到恶意网页。对于安全管理员来说,它们确实是难以检测的攻击类型。为此,本文提出了一种基于内容的网络钓鱼检测机制。在该提案中,实现了大约六种不同的机器学习模型,以选择最佳的训练模型。实验结果表明,所提出的方法鲁棒性强,对安全管理员具有可接受的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Content Based Phishing Detection with Machine Learning
In recent years due to the inevitable growth of Internet technologies, almost all the real-world systems are transferred to digital platforms. This increases the use of cyberspace in every dimension of our lives especially with mobile devices which enable us to connect to related services in anytime and anywhere concept. However, this ineluctable expansion also brings lots of security breaches especially for standard end users. Phishing is one of the mostly preferred attack types that hackers use by easily hindering themselves. This type attack is initially triggered with a simple e-mail or social media message which mainly forward the victims to a malicious webpage. For security admins, they are really hard attack types to detect. Therefore, in this paper a content based phishing detection mechanism is proposed. In the proposal about six different machine learning models are implemented to select the best training models. Experimental results show that the proposed approaches are very robust and give acceptable accuracies for security admins.
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