  The purpose of this project was to collect and analyse data about the
comparability and real-life applicability of published results focusing on
Microsoft Windows malware, more specifically the impact of dataset size and
testing dataset imbalance on measured detector performance. Some researchers
use smaller datasets, and if dataset size has a significant impact on
performance, that makes comparison of the published results difficult.
Researchers also tend to use balanced datasets and accuracy as a metric for
testing. The former is not a true representation of reality, where benign
samples significantly outnumber malware, and the latter is approach is known to
be problematic for imbalanced problems. The project identified two key
objectives, to understand if dataset size correlates to measured detector
performance to an extent that prevents meaningful comparison of published
results, and to understand if good performance reported in published research
can be expected to perform well in a real-world deployment scenario. The
research's results suggested that dataset size does correlate with measured
detector performance to an extent that prevents meaningful comparison of
published results, and without understanding the nature of the training set
size-accuracy curve for published results conclusions between approaches on
which approach is "better" shouldn't be made solely based on accuracy scores.
Results also suggested that high accuracy scores don't necessarily translate to
high real-world performance.
