  Malware is being increasingly threatening and malware detectors based on
traditional signature-based analysis are no longer suitable for current malware
detection. Recently, the models based on machine learning (ML) are developed
for predicting unknown malware variants and saving human strength. However,
most of the existing ML models are black-box, which made their pre-diction
results undependable, and therefore need further interpretation in order to be
effectively deployed in the wild. This paper aims to examine and categorize the
existing researches on ML-based malware detector interpretability. We first
give a detailed comparison over the previous work on common ML model
inter-pretability in groups after introducing the principles, attributes,
evaluation indi-cators and taxonomy of common ML interpretability. Then we
investigate the interpretation methods towards malware detection, by addressing
the importance of interpreting malware detectors, challenges faced by this
field, solutions for migitating these challenges, and a new taxonomy for
classifying all the state-of-the-art malware detection interpretability work in
recent years. The highlight of our survey is providing a new taxonomy towards
malware detection interpreta-tion methods based on the common taxonomy
summarized by previous re-searches in the common field. In addition, we are the
first to evaluate the state-of-the-art approaches by interpretation method
attributes to generate the final score so as to give insight to quantifying the
interpretability. By concluding the results of the recent researches, we hope
our work can provide suggestions for researchers who are interested in the
interpretability on ML-based malware de-tection models.
