Even though machine learning algorithms already play a significant role in data science, many current methods pose unrealistic assumptions on input data. The application of such methods is difficult due to incompatible data formats, or heterogeneous, hierarchical or entirely missing data fragments in the dataset. As a solution, we propose a versatile, unified framework called `HMill' for sample representation, model definition and training. We review in depth a multi-instance paradigm for machine learning that the framework builds on and extends. To theoretically justify the design of key components of HMill, we show an extension of the universal approximation theorem to the set of all functions realized by models implemented in the framework. The text also contains a detailed discussion on technicalities and performance improvements in our implementation, which is published for download under the MIT License. The main asset of the framework is its flexibility, which makes modelling of diverse real-world data sources with the same tool possible. Additionally to the standard setting in which a set of attributes is observed for each object individually, we explain how message-passing inference in graphs that represent whole systems of objects can be implemented in the framework. To support our claims, we solve three different problems from the cybersecurity domain using the framework. The first use case concerns IoT device identification from raw network observations. In the second problem, we study how malicious binary files can be classified using a snapshot of the operating system represented as a directed graph. The last provided example is a task of domain blacklist extension through modelling interactions between entities in the network. In all three problems, the solution based on the proposed framework achieves performance comparable to specialized approaches.
翻译:尽管机器学习算法已经在数据科学中发挥了重要作用,但许多现行方法对输入数据提出了不切实际的假设。由于数据格式不兼容,或者由于数据集中的数据分解、等级分级或完全缺失的数据碎片不相容,这些方法很难应用。作为一个解决办法,我们提议了一个称为“HMILL”的多功能、统一框架,用于样本展示、示范定义和培训。我们深入审查一个多功能模式,用于机器学习框架所建立和扩展的多功能模式。为了从理论上证明HMill关键组成部分的设计是合理的,我们展示了通用近似词的延伸,以在框架内执行的模型所实现的所有功能组合。案文还详细讨论了我们执行过程中的技术性和绩效改进,并在MIT许可证下发布供下载。作为解决办法的一个解决办法,我们提出一个称为“HMIMIL”的多功能统一框架。除了为每个对象单独观测一组属性的标准设置外,我们还解释了在图表中显示整个物体系统所实现的图解的图解。在框架内第一个框架内,为了支持我们使用一个可比较的域域域域图,我们使用三个域域域域图的模型,我们用来解的解的模型的模型,我们用三种不同的计算问题。