ABSTRACT:

With the increase in sophistication and connectedness of the healthcare networks, their attack surfaces and vulnerabilities increase significantly. Malicious agents threaten patients’ health and life by stealing or altering data as it flows among the multiple domains of healthcare networks. The problem is likely to exacerbate with the increasing use of IoT devices, edge, and core clouds in the next generation healthcare networks. Presented in this paper is MUSE, a system of deep hierarchical stacked neural networks for timely and accurate detection of malicious activity that leads to alteration of meta-information or payload of the dataflow between the IoT gateway, edge and core clouds. Smaller models at the edge clouds take substantially less time to train as compared to the large models in the core cloud. To improve the speed of training and accuracy of detection of large core cloud models, the MUSE system uses a novel method of merging and aggregating layers of trained edge cloud models to construct a partly pre-trained core cloud model. As a result, the model in the core cloud takes substantially smaller number of epochs (6 to 8) and, consequently, less time, compared to edge clouds, training of which take 35 to 40 epochs to converge. With the help of extensive evaluations, it is shown that with the MUSE system, large, merged models can be trained in significantly less time than the unmerged models that are created independently in the core cloud. Through several runs it is seen that the merged models give on an average 26.2% reduction in training times. From the experimental evaluation we demonstrate that along with fast training speeds the merged MUSE model gives high training and test accuracies, ranging from 95% to 100%, in detection of unknown attacks on dataflows. The merged model thus generalizes very well on the test data. This is a marked improvement when compared with the accuracy given by un-merged model as well as accuracy reported by other researchers with newer datasets.

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