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Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution

  • Ilia NouretdinovEmail author
  • Salaheddin Darwish
  • Stephen Wolthusen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)

Abstract

The aim of this work is to discuss abnormality detection and explanation challenges motivated by Medical Internet of Things. First, any feature is a measurement taken by a sensor at a time moment, so abnormality detection also becomes a sequential process. Second, an anomaly detection process could not rely on having a large collection of data records, but instead there is a knowledge provided by the experts.

Keywords

Anomaly explanation Untrustworthy data Internet of Things 

Notes

Acknowledgements

This work was supported by Technology Integrated Health Management (TIHM) project awarded to the School of Mathematics and Information Security at Royal Holloway as part of an initiative by NHS England supported by InnovateUK, by European Union grant 671555 (“ExCAPE"), and AstraZeneca grant R10911.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ilia Nouretdinov
    • 1
    Email author
  • Salaheddin Darwish
    • 1
  • Stephen Wolthusen
    • 1
  1. 1.Information Security GroupRHULEghamUK

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