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Even though machine learning algorithms already play a significant role indata 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 thedataset. As a solution, we propose a versatile, unified framework called`HMill' for sample representation, model definition and training. We review indepth a multi-instance paradigm for machine learning that the framework buildson 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 allfunctions realized by models implemented in the framework. The text alsocontains a detailed discussion on technicalities and performance improvementsin our implementation, which is published for download under the MIT License.The main asset of the framework is its flexibility, which makes modelling ofdiverse real-world data sources with the same tool possible. Additionally tothe standard setting in which a set of attributes is observed for each objectindividually, we explain how message-passing inference in graphs that representwhole systems of objects can be implemented in the framework. To support ourclaims, we solve three different problems from the cybersecurity domain usingthe framework. The first use case concerns IoT device identification from rawnetwork observations. In the second problem, we study how malicious binaryfiles can be classified using a snapshot of the operating system represented asa directed graph. The last provided example is a task of domain blacklistextension through modelling interactions between entities in the network. Inall three problems, the solution based on the proposed framework achievesperformance comparable to specialized approaches.

Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks