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

Qiang Liu, Tao Han, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Article number9105932
Pages (from-to)37-43
Number of pages7
JournalIEEE Network
Volume34
Issue number3
DOIs
StatePublished - May 1 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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