Purpose

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Design/methodology/approach

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Findings

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Originality/value

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.

中文翻译：

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通过超链接分析和规则提取识别网络钓鱼网站

目的

这项研究的目的是提出一种用于识别网络钓鱼网站的有效规则提取和集成方法。所提出的方法可以阐明网络钓鱼网站的模式并准确识别它们。

设计/方法/方法

超链接指示器以及基于URL的功能用于构建识别模型。在提出的方法中，首先根据各个功能提取非常简单的规则，以提供有意义且易于理解的规则。然后，使用F度量分数来选择用于识别网络钓鱼网站的高质量规则。为了构建可靠且有前途的网络钓鱼网站识别模型，使用简单的神经网络模型对所选规则进行集成。

发现

使用自我收集的基准数据集进行的实验表明，该方法在可解释性和识别性能方面优于16个常用分类器（包括7个非基于规则的分类器和4个基于规则的分类器以及5个深度学习模型）。

创意/价值

使用有效的基于规则的方法基于超链接指示器调查网络钓鱼网站的模式是创新的。它不仅有助于识别网络钓鱼网站，而且还有助于提取简单易懂的规则。