Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $\varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.

通过引入得分函数的新定义和定义一组特殊的输入变量（conformal safety set），我们分析了可扩展分类器和一致预测之间的相似性，该安全集能够识别满足误差覆盖保证的输入空间模式，即在该集合中，观察到错误（可能不安全）标签的概率受到预定义的ε误差水平的限制。通过在网络安全中应用于识别DNS隧道攻击，我们展示了该框架的实际影响。我们的工作有助于发展概率稳健可靠的机器学习模型。

概率鲁棒可伸缩机器学习分类的整体预测