  Recently researchers have proposed using deep learning-based systems for
malware detection. Unfortunately, all deep learning classification systems are
vulnerable to adversarial attacks. Previous work has studied adversarial
attacks against static analysis-based malware classifiers which only classify
the content of the unknown file without execution. However, since the majority
of malware is either packed or encrypted, malware classification based on
static analysis often fails to detect these types of files. To overcome this
limitation, anti-malware companies typically perform dynamic analysis by
emulating each file in the anti-malware engine or performing in-depth scanning
in a virtual machine. These strategies allow the analysis of the malware after
unpacking or decryption. In this work, we study different strategies of
crafting adversarial samples for dynamic analysis. These strategies operate on
sparse, binary inputs in contrast to continuous inputs such as pixels in
images. We then study the effects of two, previously proposed defensive
mechanisms against crafted adversarial samples including the distillation and
ensemble defenses. We also propose and evaluate the weight decay defense.
Experiments show that with these three defensive strategies, the number of
successfully crafted adversarial samples is reduced compared to a standard
baseline system without any defenses. In particular, the ensemble defense is
the most resilient to adversarial attacks. Importantly, none of the defenses
significantly reduce the classification accuracy for detecting malware.
Finally, we demonstrate that while adding additional hidden layers to neural
models does not significantly improve the malware classification accuracy, it
does significantly increase the classifier's robustness to adversarial attacks.
