  In cyberattack detection and prevention systems, cybersecurity analysts
always prefer solutions that are as interpretable and understandable as
rule-based or signature-based detection. This is because of the need to tune
and optimize these solutions to mitigate and control the effect of false
positives and false negatives. Interpreting machine learning models is a new
and open challenge. However, it is expected that an interpretable machine
learning solution will be domain-specific. For instance, interpretable
solutions for machine learning models in healthcare are different than
solutions in malware detection. This is because the models are complex, and
most of them work as a black-box. Recently, the increased ability for malware
authors to bypass antimalware systems has forced security specialists to look
to machine learning for creating robust detection systems. If these systems are
to be relied on in the industry, then, among other challenges, they must also
explain their predictions. The objective of this paper is to evaluate the
current state-of-the-art ML models interpretability techniques when applied to
ML-based malware detectors. We demonstrate interpretability techniques in
practice and evaluate the effectiveness of existing interpretability techniques
in the malware analysis domain.
