MAB-Learning-based hierarchical defense for secure underwater acoustic networks

Yi Zhou, Qunfei Zhang, Zhenhua Yan, Chengbing He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The underwater acoustic network (UAN) without defensive support is unreliable in an increasingly competitive marine environment. In this paper, a hierarchical defense algorithm that applies multi-armed bandit (MAB) learning is presented to combat the intelligent mobile attackers and protect the underwater relay transmissions. Specifically, the identities of the significant nodes of a network are changed periodically to hide the critical routing paths. The learning-based communication system can achieve stable links through the optimal spoofing scheme that is to mislead the attackers sensitive to the environmental feedback, and then alleviate the potential threat from invaders. Simulation results verify that the proposed defense strategy can fast reduce the outage probability of the protected objects and prolong the lifetime of underwater networks.

Original languageEnglish
Title of host publication2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages282-287
Number of pages6
ISBN (Electronic)9781665439442
DOIs
StatePublished - 28 Jul 2021
Event2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021 - Xiamen, China
Duration: 28 Jul 202130 Jul 2021

Publication series

Name2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021

Conference

Conference2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021
Country/TerritoryChina
CityXiamen
Period28/07/2130/07/21

Keywords

  • Multi-armed bandit learning
  • Network security
  • Underwater acoustic network

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