Representation Learning for Sensor-based Device Pairing

Ngu Nguyen; Nico Jähne-Raden; Ulf Kulau; Stephan Sigg

doi:10.1109/PERCOMW.2018.8480412

Representation Learning for Sensor-based Device Pairing

Ngu Nguyen, Nico Jähne-Raden, Ulf Kulau, Stephan Sigg

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

375 Downloads (Pure)

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018
Publisher	IEEE
Pages	508-511
Number of pages	4
ISBN (Electronic)	9781538632277
DOIs	https://doi.org/10.1109/PERCOMW.2018.8480412
Publication status	Published - 2 Oct 2018
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Pervasive Computing and Communications Workshops - Athens, Greece Duration: 19 Mar 2018 → 23 Mar 2018

Conference

Conference	IEEE International Conference on Pervasive Computing and Communications Workshops
Abbreviated title	PerCom Workshops
Country/Territory	Greece
City	Athens
Period	19/03/2018 → 23/03/2018

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10.1109/PERCOMW.2018.8480412

ELEC_Nguyen_Representation Learning for Sensor-based Device PairingAccepted author manuscript, 422 KB

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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 = oct,

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doi = "10.1109/PERCOMW.2018.8480412",

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booktitle = "2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops ; Conference date: 19-03-2018 Through 23-03-2018",

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

Representation Learning for Sensor-based Device Pairing. / Nguyen, Ngu; Jähne-Raden, Nico; Kulau, Ulf et al.
2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. IEEE, 2018. p. 508-511 8480412.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Representation Learning for Sensor-based Device Pairing

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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.

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T2 - IEEE International Conference on Pervasive Computing and Communications Workshops

Y2 - 19 March 2018 through 23 March 2018

ER -

Representation Learning for Sensor-based Device Pairing

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