Representation Learning for Sensor-based Device Pairing

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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Representation Learning for Sensor-based Device Pairing. / Nguyen, Ngu; Jähne-Raden, Nico; Kulau, Ulf; Sigg, Stephan.

2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. Institute of Electrical and Electronics Engineers, 2018. s. 508-511 8480412.

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Nguyen, N, Jähne-Raden, N, Kulau, U & Sigg, S 2018, Representation Learning for Sensor-based Device Pairing. julkaisussa 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018., 8480412, Institute of Electrical and Electronics Engineers, Sivut 508-511, Athens, Kreikka, 19/03/2018. https://doi.org/10.1109/PERCOMW.2018.8480412

APA

Nguyen, N., Jähne-Raden, N., Kulau, U., & Sigg, S. (2018). Representation Learning for Sensor-based Device Pairing. teoksessa 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 (Sivut 508-511). [8480412] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PERCOMW.2018.8480412

Vancouver

Nguyen N, Jähne-Raden N, Kulau U, Sigg S. Representation Learning for Sensor-based Device Pairing. julkaisussa 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. Institute of Electrical and Electronics Engineers. 2018. s. 508-511. 8480412 https://doi.org/10.1109/PERCOMW.2018.8480412

Author

Nguyen, Ngu ; Jähne-Raden, Nico ; Kulau, Ulf ; Sigg, Stephan. / Representation Learning for Sensor-based Device Pairing. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. Institute of Electrical and Electronics Engineers, 2018. Sivut 508-511

Bibtex - Lataa

@inproceedings{03f9bee85e8046d5a6596b2f22a87dc7,
title = "Representation Learning for Sensor-based Device Pairing",
abstract = "The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.",
author = "Ngu Nguyen and Nico J{\"a}hne-Raden and Ulf Kulau and Stephan Sigg",
year = "2018",
month = "10",
day = "2",
doi = "10.1109/PERCOMW.2018.8480412",
language = "English",
pages = "508--511",
booktitle = "2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

RIS - Lataa

TY - GEN

T1 - Representation Learning for Sensor-based Device Pairing

AU - Nguyen, Ngu

AU - Jähne-Raden, Nico

AU - Kulau, Ulf

AU - Sigg, Stephan

PY - 2018/10/2

Y1 - 2018/10/2

N2 - The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.

AB - The emergence of on-body gadgets has introduced a novel research direction: unobtrusive and continuous device pairing. Existing approaches leveraged contextual information collected by sensors to generate secure communication keys. The secret information is represented throught hand-engineered features. In this paper, we propose a learning method based on Siamese neural networks to extract features that signify on-body context while separating off-body devices.

UR - http://www.scopus.com/inward/record.url?scp=85056484633&partnerID=8YFLogxK

U2 - 10.1109/PERCOMW.2018.8480412

DO - 10.1109/PERCOMW.2018.8480412

M3 - Conference contribution

SP - 508

EP - 511

BT - 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018

PB - Institute of Electrical and Electronics Engineers

ER -

ID: 29890958