  Supervised Deep Learning requires plenty of labeled data to converge, and
hence perform optimally for task-specific learning. Therefore, we propose a
novel mechanism named DRo (for Deep Routing) for data-scarce domains like
security. The DRo approach builds upon some of the recent developments in
Deep-Clustering. In particular, it exploits the self-augmented training
mechanism using synthetically generated local perturbations. DRo not only
allays the challenges with sparse-labeled data but also offers many unique
advantages. We also developed a system named DRoID that uses the DRo mechanism
for enhancing the performance of an existing Malware Detection System that uses
(low information features like the) Android implicit Intent(s) as the only
features. We conduct experiments on DRoID using a popular and standardized
Android malware dataset and found that the DRo mechanism could successfully
reduce the false-alarms generated by the downstream classifier by 67.9%, and
also simultaneously boosts its accuracy by 11.3%. This is significant not only
because the gains achieved are unparalleled but also because the features used
were never considered rich enough to train a classifier on; and hence no decent
performance could ever be reported by any malware classification system
till-date using these features in isolation. Owing to the results achieved, the
DRo mechanism claims a dominant position amongst all known systems that aims to
enhance the classification performance of deep learning models with
sparse-labeled data.

Cryptography and Security;Artificial Intelligence;Machine Learning