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

Ilia Nouretdinov; Salaheddin Darwish; Stephen Wolthusen

doi:10.1007/978-3-319-94523-1_31

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

Ilia Nouretdinov
, Salaheddin Darwish
, Stephen Wolthusen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

105 Downloads (Pure)

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.

Original language	English
Title of host publication	16th International Conference On Smart homes and health Telematics (ICOST'2018)
Publisher	Springer
Pages	310-316
Number of pages	7
ISBN (Electronic)	978-3-319-94523-1
ISBN (Print)	978-3-319-94522-4
DOIs	https://doi.org/10.1007/978-3-319-94523-1_31
Publication status	Published - 2018
Event	16th International Conference On Smart homes and health Telematics: ICOST - Singapore Duration: 10 Jul 2018 → 12 Jul 2018 http://www.icostconference.org/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10898

Conference

Conference	16th International Conference On Smart homes and health Telematics
Period	10/07/18 → 12/07/18
Internet address	http://www.icostconference.org/

Keywords

anomaly explanation
untrustworthy data
Internet of Things

Access to Document

10.1007/978-3-319-94523-1_31

Accepted ManuscriptAccepted author manuscript, 419 KB

http://www.icostconference.org/

Cite this

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title = "Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution",

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. ",

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Nouretdinov, I, Darwish, S & Wolthusen, S 2018, Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution. in 16th International Conference On Smart homes and health Telematics (ICOST'2018). Lecture Notes in Computer Science, vol. 10898, Springer, pp. 310-316, 16th International Conference On Smart homes and health Telematics, 10/07/18. https://doi.org/10.1007/978-3-319-94523-1_31

Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution. / Nouretdinov, Ilia; Darwish, Salaheddin; Wolthusen, Stephen.
16th International Conference On Smart homes and health Telematics (ICOST'2018). Springer, 2018. p. 310-316 (Lecture Notes in Computer Science; Vol. 10898).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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