Evidence map›Paper›PMID 30416937›Full record

ArticleComputational social networks2018

Social learning for resilient data fusion against data falsification attacks.

Fernando Rosas, Kwang-Cheng Chen, Deniz Gündüz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational social networks, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Fernando Rosas1Centre of Complexity Science and Department of Mathematics, Imperial College London, Kensington, London, SW72AZ UK.ORCID 0000-0001-7790-6183
Kwang-Cheng Chen3Department of Electrical Engineering, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620 USA.
Deniz Gündüz2Department of Electrical and Electronic Engineering, Imperial College London, Kensington, London, SW72AZ UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternet 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.

methodsTo 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.

resultsWe 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.

conclusionsOur results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.

Indexed as

Byzantine nodesCollective behaviourData falsification attacksData fusionDistributed decision-makingInformation cascadesMulti-agent systemsSensor networksSocial learningSocial networks

Identifiers

PMID30416937
PMCPMC6208803

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.