  Effective and efficient mitigation of malware is a long-time endeavor in the
information security community. The development of an anti-malware system that
can counteract an unknown malware is a prolific activity that may benefit
several sectors. We envision an intelligent anti-malware system that utilizes
the power of deep learning (DL) models. Using such models would enable the
detection of newly-released malware through mathematical generalization. That
is, finding the relationship between a given malware $x$ and its corresponding
malware family $y$, $f: x \mapsto y$. To accomplish this feat, we used the
Malimg dataset (Nataraj et al., 2011) which consists of malware images that
were processed from malware binaries, and then we trained the following DL
models 1 to classify each malware family: CNN-SVM (Tang, 2013), GRU-SVM
(Agarap, 2017), and MLP-SVM. Empirical evidence has shown that the GRU-SVM
stands out among the DL models with a predictive accuracy of ~84.92%. This
stands to reason for the mentioned model had the relatively most sophisticated
architecture design among the presented models. The exploration of an even more
optimal DL-SVM model is the next stage towards the engineering of an
intelligent anti-malware system.
