  Deep learning (DL) models for image-based malware detection have exhibited
their capability in producing high prediction accuracy. But model
interpretability is posing challenges to their widespread application in
security and safety-critical application domains. This paper aims for designing
an Interpretable Ensemble learning approach for image-based Malware Detection
(IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL)
detector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach,
which can give the pixel-level explanation to SDEL detector outputs.
Furthermore, we develop formulas for calculating fidelity, robustness and
expressiveness on pixel-level heatmaps in order to assess the quality of EDTD
explanation. With EDTD explanation, we develop a novel Interpretable Dropout
approach (IDrop), which establishes IEMD by training SDEL detector. Experiment
results exhibit the better explanation of our EDTD than the previous
explanation methods for image-based malware detection. Besides, experiment
results indicate that IEMD achieves a higher detection accuracy up to 99.87%
while exhibiting interpretability with high quality of prediction results.
Moreover, experiment results indicate that IEMD interpretability increases with
the increasing detection accuracy during the construction of IEMD. This
consistency suggests that IDrop can mitigate the tradeoff between model
interpretability and detection accuracy.
