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Blockchain-Based GNSS Spoofing Detection for Multiple UAV Systems

Authors: Rui Han 0002; Lin Bai 0001; Jianwei Liu 0001; Peng Chen;

Blockchain-Based GNSS Spoofing Detection for Multiple UAV Systems

Abstract

With the rapid growth of unmanned aerial vehicle (UAV), there is now an increasing interest in the security of UAV systems. There exist various attacks which have threatened the security of UAV systems, in terms of the global navigation satellite system (GNSS) spoofing. Consisted of multiple UAVs, the flying ad hoc networks (FANET) have been studied to extend the employment and coverage of UAV systems. Based on the connectivity and information interaction of FANET, in this article, the blockchain technology is applied to detect GNSS signal attacks for UAV systems. In particular, we first introduce an overview of the GNSS security of UAV systems and the principles for blockchain technology. Based on the principles, a logical architecture is proposed, where blockchain is taken into consideration for GNSS spoofing detection. Performance analysis verifies that the proposed GNSS spoofing detection system can be used effectively. Finally, several challenges in blockchain are discussed, including security, cost, and regulation.

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Top 10%
Top 10%
Top 10%
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