Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems

Document Type

Article

Publication Date

5-1-2020

Abstract

There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Due to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.

Identifier

85086143406 (Scopus)

Publication Title

IEEE Network

External Full Text Location

https://doi.org/10.1109/MNET.011.1900303

e-ISSN

1558156X

ISSN

08908044

First Page

37

Last Page

43

Issue

3

Volume

34

Grant

1731675

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