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.

翻译：受监督的深层学习需要大量标签数据才能汇集,从而最优化地进行任务特定的学习。 因此, 我们提出一个新机制, 名为 DRo( 深路运行), 用于安全等数据保密域。 DRo 方法以深封闭域中最近的一些发展为基础。 特别是, 它利用合成生成的本地扰动来利用自增强的培训机制。 DRo 机制不仅通过稀释标签数据来缓解挑战,而且同时提供许多独特的优势。 我们还开发了一个名为 DRoID 的系统, 使用 DRoID 机制来增强现有的Malwarard 检测系统的性能, 该系统使用( 低信息特性, 如安全 ) 和机器人隐含 Intent ( ) 来作为唯一的特性。 我们使用流行和标准化的恶意软件数据集对 DROID 进行实验, 发现 DRo 机制可以成功地将下游分类器生成的假标减少67.9%, 并且同时将其精确度提高11. 3 % 。 这很重要, 不仅因为所实现的DRO 机制是无比喻,, 是因为所实现的DROward rove rode rovely rode rode roal registration 的成绩是前所未有的定位, 。