As a new form of malicious software, phishing websites appear frequently in recent years, which cause great harm to online financial services and data security. In this paper, we design and implement an intelligent model for detecting phishing websites. In this model, we extract 10 different types of features such as title, keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been employed for automatic phishing categorization. Case studies on large and real daily phishing websites collected from King soft Internet Security Lab demonstrate that our proposed model outperforms other commonly used anti-phishing methods and tools in phishing website detection.
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机译:网络钓鱼网站作为一种新型的恶意软件,近年来屡见不鲜,对在线金融服务和数据安全造成了极大的伤害。在本文中,我们设计并实现了一种用于检测网络钓鱼网站的智能模型。在此模型中,我们提取了10种不同类型的功能,例如标题,关键字和链接文本信息来表示网站。然后根据这些不同的特征构建异构分类器。我们提出了一种有原则的集成分类算法,以结合来自不同网络钓鱼检测分类器的预测结果。分层聚类技术已被用于自动网络钓鱼分类。从King Soft Internet Security Lab收集的大型和实际日常网络钓鱼网站的案例研究表明,我们提出的模型在网络钓鱼网站检测方面优于其他常用的反网络钓鱼方法和工具。
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