Slice Isolation Through Classification: A New Dimension for 5G Network Slicing Security

Pp: 474-483 (10)

Abstract

Connectivity over the internet is drastically increasing which is the main factor for developing different generations in the mobile network. Earlier, the usage of the internet was primarily through smartphones but now with the invention of IoT devices, the shift has been changed into different sectors like healthcare, agriculture, infrastructures, and vehicles using this internet. With this shift the demand for bandwidth, connectivity has been increased. And can be resolved through 5G network slicing. In this paper, we proposed a slice isolation model for the security of 5G network slicing also it will help users utilize the characteristics of network slicing to the fullest. Our proposed model uses a Machine learning algorithm to perform slice isolation.

Keywords: Decision Tree, eMBB, k-Nearest Neighbor, Support Vector Machine, mMTC, Random Forest, uRLLC.

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