  Machine learning (ML) has become an important paradigm for cyberthreat
detection (CTD) in the recent years. A substantial research effort has been
invested in the development of specialized algorithms for CTD tasks. From the
operational perspective, however, the progress of ML-based CTD is hindered by
the difficulty in obtaining the large sets of labelled data to train ML
detectors. A potential solution to this problem are semisupervised learning
(SsL) methods, which combine small labelled datasets with large amounts of
unlabelled data.
  This paper is aimed at systematization of existing work on SsL for CTD and,
in particular, on understanding the utility of unlabelled data in such systems.
To this end, we analyze the cost of labelling in various CTD tasks and develop
a formal cost model for SsL in this context. Building on this foundation, we
formalize a set of requirements for evaluation of SsL methods, which elucidates
the contribution of unlabelled data. We review the state-of-the-art and observe
that no previous work meets such requirements. To address this problem, we
propose a framework for assessing the benefits of unlabelled data in SsL. We
showcase an application of this framework by performing the first benchmark
evaluation that highlights the tradeoffs of 9 existing SsL methods on 9 public
datasets. Our findings verify that, in some cases, unlabelled data provides a
small, but statistically significant, performance gain. This paper highlights
that SsL in CTD has a lot of room for improvement, which should stimulate
future research in this field.
