Machine-learning phishing webpage detectors (ML-PWD) have been shown to
suffer from adversarial manipulations of the HTML code of the input webpage.
Nevertheless, the attacks recently proposed have demonstrated limited
effectiveness due to their lack of optimizing the usage of the adopted
manipulations, and they focus solely on specific elements of the HTML code. In
this work, we overcome these limitations by first designing a novel set of
fine-grained manipulations which allow to modify the HTML code of the input
phishing webpage without compromising its maliciousness and visual appearance,
i.e., the manipulations are functionality- and rendering-preserving by design.
We then select which manipulations should be applied to bypass the target
detector by a query-efficient black-box optimization algorithm. Our experiments
show that our attacks are able to raze to the ground the performance of current
state-of-the-art ML-PWD using just 30 queries, thus overcoming the weaker
attacks developed in previous work, and enabling a much fairer robustness
evaluation of ML-PWD.

通过黑盒优化算法，我们设计了一种新颖的细粒度修改方式，能够在不损害恶意性和视觉外观的情况下修改输入钓鱼网页的 HTML 代码，从而成功绕过目标检测器，破坏当前最先进的机器学习防钓鱼网页检测系统的性能，仅使用 30 个查询就能击败以前的攻击方法，并实现对机器学习防钓鱼网页检测系统更公正的鲁棒性评估。

夷为平地：机器学习钓鱼网页检测器上高效的敌对 HTML 攻击

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on  Machine-Learning Phishing Webpage Detectors

We present a functional framework for automated mechanism design based on a
two-stage game model of strategic interaction between the designer and the
mechanism participants, and apply it to several classes of two-player infinite
games of incomplete information. At the core of our framework is a black-box
optimization algorithm which guides the selection process of candidate
mechanisms. Our approach yields optimal or nearly optimal mechanisms in several
application domains using various objective functions. By comparing our results
with known optimal mechanisms, and in some cases improving on the best known
mechanisms, we provide evidence that ours is a promising approach to parametric
design of indirect mechanisms.

提出了一个基于两阶段博弈模型的自动化机制设计功能框架，并将其应用于几类不完全信息的两人无穷博弈中。我们的方法使用黑盒优化算法，可以实现优化或接近最优化的机制设计，并在多种应用领域得到了验证。通过与已知的最优机制进行比较，我们证明了该方法是参数化设计间接机制的一个有前途的方法。