Social learning against data falsification in sensor networks

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Title: Social learning against data falsification in sensor networks
Authors: Rosas De Andraca, F
Chen, K-C
Item Type: Conference Paper
Abstract: Sensor networks generate large amounts of geographically-distributed data. The conventional approach to exploit this data is to first gather it in a special node that then performs processing and inference. However, what happens if this node is destroyed, or even worst, if it is hijacked? To explore this problem, in this work we consider a smart attacker who can take control of critical nodes within the network and use them to inject false information. In order to face this critical security thread, we propose a novel scheme that enables data aggregation and decision-making over networks based on social learning, where the sensor nodes act resembling how agents make decisions in social networks. Our results suggest that social learning enables high network resilience, even when a significant portion of the nodes have been compromised by the attacker.
Issue Date: 27-Nov-2017
Date of Acceptance: 7-Oct-2017
ISBN: 978-3-319-72149-1
ISSN: 1860-949X
Publisher: Springer Verlag
Start Page: 704
End Page: 716
Journal / Book Title: Studies in Computational Intelligence
Volume: 689
Copyright Statement: © Springer International Publishing AG 2018. The final publication is available at Springer via
Conference Name: Conference on Complex Networks 2017
Keywords: Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2017-11-29
Finish Date: 2017-12-01
Conference Place: Lyon, France
Online Publication Date: 2017-11-27
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences

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