  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.
