Social learning for resilient data fusion against data falsification attacks

Social learning for resilient data fusion against data falsification attacks

Abstract Background Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to...

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Bibliographic Details
Main Authors: Fernando Rosas, Kwang-Cheng Chen, Deniz Gündüz
Format: Article
Language:English
Published: SpringerOpen 2018-10-01
Series:Computational Social Networks
Subjects:
Distributed decision-making
Data fusion
Sensor networks
Social networks
Data falsification attacks
Byzantine nodes
Online Access:http://link.springer.com/article/10.1186/s40649-018-0057-7

Internet

http://link.springer.com/article/10.1186/s40649-018-0057-7

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