Phishing URL Detection Using URL Ranking
Phishing URL Detection Using URL Ranking
复制标题
使用 URL 排名检测网络钓鱼 URL
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Mengel
中科院分区：
文献类型：
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作者：
M. Feroz;S. Mengel
Web的开放性为犯罪分子提供了上传恶意内容的机会。事实上，尽管进行了广泛的研究，基于电子邮件的垃圾邮件过滤技术无法保护其他Web服务。因此，必须采取跨Web服务进行泛化的对策，以保护用户免受网络钓鱼主机URL的侵害。本文描述了一种根据URL的词汇特征和基于主机的特征自动对URL进行分类的方法。对整个数据集执行聚类，并为每个URL导出聚类ID（或标签），这反过来又被分类系统用作预测特征。在线URL信誉服务用于对URL进行分类，并且返回的类别用作补充信息源，该补充信息源将使系统能够对URL进行排名。该分类器通过检测大量钓鱼主机实现了93-98%的准确率，同时保持了适度的误报率。URL聚类、URL分类和URL分类机制结合起来为URL提供一个排名。
The openness of the Web exposes opportunities for criminals to upload malicious content. In fact, despite extensive research, email based spam filtering techniques are unable to protect other web services. Therefore, a counter measure must be taken that generalizes across web services to protect the user from phishing host URLs. This paper describes an approach that classifies URLs automatically based on their lexical and host-based features. Clustering is performed on the entire dataset and a cluster ID (or label) is derived for each URL, which in turn is used as a predictive feature by the classification system. Online URL reputation services are used in order to categorize URLs and the categories returned are used as a supplemental source of information that would enable the system to rank URLs. The classifier achieves 93-98% accuracy by detecting a large number of phishing hosts, while maintaining a modest false positive rate. URL clustering, URL classification, and URL categorization mechanisms work in conjunction to give URLs a rank.
DOI：
10.1007/3-540-49430-8
发表时间：
2002
期刊：
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影响因子：
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作者：
J. Hartmanis;Takeo Kanade
通讯作者：
J. Hartmanis;Takeo Kanade