Social learning for resilient data fusion against data falsification attacks
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Published version
Author(s)
Rosas De Andraca, F
Chen, Kwang-Cheng
Gunduz, Deniz
Type
Journal Article
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 be exploited by smart attackers.
Methods
To tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.
Results
We analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.
Conclusions
Our results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.
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 be exploited by smart attackers.
Methods
To tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.
Results
We analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.
Conclusions
Our results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.
Date Issued
2018-10-25
Date Acceptance
2018-09-21
Citation
Computational Social Networks, 2018, 5
ISSN
2197-4314
Publisher
SpringerOpen
Journal / Book Title
Computational Social Networks
Volume
5
Copyright Statement
© 2018 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
License URL
Sponsor
Commission of the European Communities
Commission of the European Communities
Grant Number
702981
677854
Subjects
Byzantine nodes
Collective behaviour
Data falsification attacks
Data fusion
Distributed decision-making
Information cascades
Multi-agent systems
Sensor networks
Social learning
Social networks
Publication Status
Published
Article Number
10