论文标题
改善同型攻击分类
Improving Homograph Attack Classification
论文作者
论文摘要
视觉同型攻击是攻击者通过利用看起来与真正域相似的锻造域来欺骗Web用户访问哪个域的一种方式。 T. Thao等。 （IFIP SEC'19）通过对从单一基于单个字符的结构相似性指数（SSIM）提取的特征应用传统的监督学习算法提出了同派分类。本文旨在通过将其SSIM特征与从N-Cram模型中提取的199个功能相结合并应用高级合奏学习算法来提高分类精度。实验结果表明，我们提出的方法甚至可以提高精度的1.81％，并降低假阳性率的2.15％。此外，现有的工作应用机器在某些功能上学习，而无法解释为什么应用它可以提高准确性。即使可以提高准确性，理解地面也是至关重要的。因此，在本文中，我们进行了错误的经验分析，并可以在我们提出的方法背后获得一些发现。
A visual homograph attack is a way that the attacker deceives the web users about which domain they are visiting by exploiting forged domains that look similar to the genuine domains. T. Thao et al. (IFIP SEC'19) proposed a homograph classification by applying conventional supervised learning algorithms on the features extracted from a single-character-based Structural Similarity Index (SSIM). This paper aims to improve the classification accuracy by combining their SSIM features with 199 features extracted from a N-gram model and applying advanced ensemble learning algorithms. The experimental result showed that our proposed method could enhance even 1.81% of accuracy and reduce 2.15% of false-positive rate. Furthermore, existing work applied machine learning on some features without being able to explain why applying it can improve the accuracy. Even though the accuracy could be improved, understanding the ground-truth is also crucial. Therefore, in this paper, we conducted an error empirical analysis and could obtain several findings behind our proposed approach.
