  Over the years, most research towards defenses against adversarial attacks on
machine learning models has been in the image recognition domain. The ML-based
malware detection domain has received less attention despite its importance.
Moreover, most work exploring these defenses has focused on several methods but
with no strategy when applying them. In this paper, we introduce StratDef,
which is a strategic defense system based on a moving target defense approach.
We overcome challenges related to the systematic construction, selection, and
strategic use of models to maximize adversarial robustness. StratDef
dynamically and strategically chooses the best models to increase the
uncertainty for the attacker while minimizing critical aspects in the
adversarial ML domain, like attack transferability. We provide the first
comprehensive evaluation of defenses against adversarial attacks on machine
learning for malware detection, where our threat model explores different
levels of threat, attacker knowledge, capabilities, and attack intensities. We
show that StratDef performs better than other defenses even when facing the
peak adversarial threat. We also show that, of the existing defenses, only a
few adversarially-trained models provide substantially better protection than
just using vanilla models but are still outperformed by StratDef.
