  Dynamic analysis enables detecting Windows malware by executing programs in a
controlled environment, and storing their actions in log reports. Previous work
has started training machine learning models on such reports to perform either
malware detection or malware classification. However, most of the approaches
(i) have only considered convolutional and long-short term memory networks,
(ii) they have been built focusing only on APIs called at runtime, without
considering other relevant though heterogeneous sources of information like
network and file operations, and (iii) the code and pretrained models are
hardly available, hindering reproducibility of results in this research area.
In this work, we overcome these limitations by presenting Nebula, a versatile,
self-attention transformer-based neural architecture that can generalize across
different behavior representations and formats, combining heterogeneous
information from dynamic log reports. We show the efficacy of Nebula on three
distinct data collections from different dynamic analysis platforms, comparing
its performance with previous state-of-the-art models developed for malware
detection and classification tasks. We produce an extensive ablation study that
showcases how the components of Nebula influence its predictive performance,
while enabling it to outperform some competing approaches at very low false
positive rates. We conclude our work by inspecting the behavior of Nebula
through the application of explainability methods, which highlight that Nebula
correctly focuses more on portions of reports that contain malicious
activities. We release our code and models at github.com/dtrizna/nebula.
