从品牌的制高点打击网络钓鱼

V. Bulakh, Minaxi Gupta
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引用次数: 6

摘要

目前存在的大多数反网络钓鱼解决方案都需要扫描网络的很大一部分,这是巨大的,相当于大海捞针。此外,这样的解决方案不是很有效。我们提出了一种不同的方法。我们的解决方案不依赖于扫描整个互联网或大部分互联网,只需要访问品牌的流量,以便能够检测针对该品牌的网络钓鱼企图。通过分析网络钓鱼网站的样本,我们发现了可以用来区分网络钓鱼网站和合法网站的特征。然后,我们使用这些特征来训练机器学习分类器,该分类器能够帮助品牌检测针对它们的网络钓鱼企图。我们的方法可以检测到高达86%的针对品牌的网络钓鱼攻击,最好作为现有反网络钓鱼解决方案的补充工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Countering Phishing from Brands' Vantage Point
Most anti-phishing solutions that exist today require scanning a large portion of the web, which is vast and equivalent to finding a needle in a haystack. In addition, such solutions are not very efficient. We propose a different approach. Our solution does not rely on the scanning of the entire Internet or a large portion of it and only needs access to the brand's traffic in order to be able to detect phishing attempts against that brand. By analyzing a sample of phishing websites, we find features that can be used to distinguish phishing websites from the legitimate ones. We then use these features to train a machine learning classifier capable of helping brands detect phishing attempts against them. Our approach can detect up to 86% of phishing attacks against the brands and is best used as a complementary tool to the existing anti-phishing solutions.
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