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首页> 外文期刊>International Journal of Intelligent Systems >Advanced evasion attacks and mitigations on practical ML-based phishing website classifiers
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Advanced evasion attacks and mitigations on practical ML-based phishing website classifiers

机译：基于实际ML的网络钓鱼网站分类器的高级逃避攻击和缓解

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摘要

Machine learning (ML) based classifiers are vulnerable to evasion attacks, as shown by recent attacks. However, there is a lack of systematic study of evasion attacks on ML-based anti-phishing detection. In this study, we show that evasion attacks are not only effective on practical ML-based classifiers, but can also be efficiently launched without destructing the functionalities and appearance. For this purpose, we propose three mutation-based attacks, differing in the knowledge of the target classifier, addressing a key technical challenge: automatically crafting an adversarial sample from a known phishing website in a way that can mislead classifiers. To launch attacks in the white- and gray-box scenarios, we also propose a sample-based collision attack to gain the knowledge of the target classifier. We demonstrate the efficacy of our evasion attacks on the state-of-the-art, Google's phishing page filter, achieved 100% attack success rate in less than one second per website. Moreover, the transferability attack on BitDe-fender's industrial phishing page classifier, TrafficLight, achieved up to 81.25% attack success rate. We further propose a similarity-based method to mitigate such evasion attacks, Pelican, which compares the similarity of an unknown website with recently detected phishing websites. We demonstrate that Pelican can effectively detect evasion attacks, hence could be integrated into ML-based classifiers. We also highlight two strategies of classification rule selection to enhance the robustness of classifiers. Our findings contribute to design more robust phishing website classifiers in practice.
机译：基于机器学习（ML）的分类器容易受到逃离的攻击，如最近的攻击所示。然而，缺乏对ML的抗网络训练检测的逃避攻击的系统研究。在这项研究中，我们表明逃避攻击不仅有效地对基于实际的基于ML的分类器，而且可以有效地推动，而不会破坏功能和外观。为此目的，我们提出了三种基于突变的攻击，与目标分类器的知识不同，解决了一个关键的技术挑战：自动从一个可以误导分类器的方式从已知的网络钓鱼网站制作对抗性样本。为了在白细胞和灰度方案中发射攻击，我们还提出了一种基于样本的碰撞攻击，以获得目标分类器的知识。我们展示了我们逃离攻击对最先进的谷歌的网络钓鱼页面过滤器的功效，在每位网站不到一秒钟内实现了100％的攻击成功率。此外，对Bitde-Fender的工业网络钓鱼页面分类器，交通的可转移性攻击达到81.25％的攻击成功率。我们进一步提出了一种基于相似性的方法，以减轻这种逃离攻击鹈鹕，这与最近检测到的网络钓鱼网站的未知网站的相似性进行了比较。我们证明鹈鹕可以有效地检测逃离攻击，因此可以集成到基于ML的分类器中。我们还突出了两种分类规则选择策略，以增强分类器的稳健性。我们的调查结果有助于在实践中设计更强大的网络钓鱼网站分类器。

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