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 proceedingConference contribution

Published

Standard

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 proceedingConference contribution

Harvard

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. DOI: 10.1007/978-3-319-94523-1_31

APA

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) (pp. 310-316). (Lecture Notes in Computer Science; Vol. 10898). Springer. DOI: 10.1007/978-3-319-94523-1_31

Vancouver

Nouretdinov I, Darwish S, Wolthusen S. Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution. In 16th International Conference On Smart homes and health Telematics (ICOST'2018). Springer. 2018. p. 310-316. (Lecture Notes in Computer Science). Available from, DOI: 10.1007/978-3-319-94523-1_31

Author

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

BibTeX

@inproceedings{48a054c80f8644fcbb27ea61416cbe36,
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.",
keywords = "anomaly explanation, untrustworthy data, Internet of Things",
author = "Ilia Nouretdinov and Salaheddin Darwish and Stephen Wolthusen",
year = "2018",
doi = "10.1007/978-3-319-94523-1_31",
language = "English",
isbn = "978-3-319-94522-4",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "310--316",
booktitle = "16th International Conference On Smart homes and health Telematics (ICOST'2018)",

}

RIS

TY - GEN

T1 - Detection of Untrustworthy IoT Measurements Using Expert Knowledge of Their Joint Distribution

AU - Nouretdinov,Ilia

AU - Darwish,Salaheddin

AU - Wolthusen,Stephen

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - anomaly explanation

KW - untrustworthy data

KW - Internet of Things

U2 - 10.1007/978-3-319-94523-1_31

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

M3 - Conference contribution

SN - 978-3-319-94522-4

T3 - Lecture Notes in Computer Science

SP - 310

EP - 316

BT - 16th International Conference On Smart homes and health Telematics (ICOST'2018)

PB - Springer

ER -