bandana — body area network device-to-device authentication … — body area network...

BANDANA — Body Area Network Device-to-device Authentication using Natural gAit Ngu Nguyen a , Caglar Yuce Kaya b , Dominik Schürmann b , Arne Brüsch b , Stephan Sigg a , Lars Wolf b a Aalto University b Technische Universität Braunschweig {le.ngu.nguyen, stephan.sigg}@aalto.fi {kaya, schuermann, bruesch, wolf}@ibr.cs.tu-bs.de Introduction We introduce BANDANA, a solution for continuous device-to-device authentication in Body Area Networks. Wearable sensors (including: accelerometer, gyroscope, and magnetometer) are utilised to collect body movement data. This data is used to secure a communication between two or more devices. We process the continuously-captured signals to extract gait information when users are walking. The proposed method exploits user and time dependent gait fluctuation to generate gait fingerprints. Our experiments show that our protocol can produce secure communication keys for on-body devices. Experiments Experimented with 2 datasets: Mannheim (Sztyler et al. in PerCom 2016, 15 subjects, 7 sensor locations) & Osaka (Ngo et al. in Pattern Recognition 2014, 482 subjects, 3 sensors locations) Mannheim dataset: Intra-body (same body) and inter-body (different bodies) results Demo Publications ● Dominik Schürmann, Arne Brüsch, Stephan Sigg, and Lars Wolf. “BANDANA – Body Area Network Device-to-device Authentication using Natural gAit”. In: IEEE International Conference on Pervasive Computing and Communications (PerCom) . Mar. 2017, pp. 190–196 ● Dominik Schürmann, Arne Brüsch, Ngu Nguyen, Stephan Sigg, and Lars Wolf. “Moves like Jagger: Exploiting variations in instantaneous gait for spontaneous device pairing”. Submitted to: Pervasive and Mobile Computing. 2018 ● Arne Brüsch, Ngu Nguyen, Dominik Schürmann, Stephan Sigg, and Lars Wolf. “On the secrecy of publicly observable biometric features: security properties of gait for mobile device pairing”. Submitted to: IEEE Transactions on Mobile Computing (TMC). 2018 Acknowledgments We appreciate partial funding from an EIT Digital HII Active project, Academy of Finland and the German Academic Exchange Service (DAAD). Quantization Algorithm Devices on the same body Devices on different bodies Osaka dataset: Intra-body (same body) and inter-body (different bodies) results Components BANDANA’s quantization algorithm compares instantaneous gait acceleration to the mean gait acceleration to derive binary gait fingerprints Secure communication between smartwatch and smartphone Implementation

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Page 1: BANDANA — Body Area Network Device-to-device Authentication … — Body Area Network Device-to-device Authentication using Natural gAit Ngu Nguyena, Caglar Yuce Kayab, Dominik Schürmannb,

BANDANA — Body Area Network Device-to-device Authentication using Natural gAit

Ngu Nguyena, Caglar Yuce Kayab, Dominik Schürmannb, Arne Brüschb, Stephan Sigga, Lars Wolfb

a Aalto University b Technische Universität Braunschweig{le.ngu.nguyen, stephan.sigg}@aalto.fi {kaya, schuermann, bruesch, wolf}@ibr.cs.tu-bs.de

IntroductionWe introduce BANDANA, a solution for continuous device-to-device authentication in Body Area Networks. Wearable sensors (including: accelerometer, gyroscope, and magnetometer) are utilised to collect body movement data. This data is used to secure a communication between two or more devices.

We process the continuously-captured signals to extract gait information when users are walking. The proposed method exploits user and time dependent gait fluctuation to generate gait fingerprints. Our experiments show that our protocol can produce secure communication keys for on-body devices.

ExperimentsExperimented with 2 datasets: Mannheim (Sztyler et al. in PerCom 2016, 15 subjects, 7 sensor locations) & Osaka (Ngo et al. in Pattern Recognition 2014, 482 subjects, 3 sensors locations)

Mannheim dataset: Intra-body (same body) and inter-body (different bodies) results

Demo

Publications● Dominik Schürmann, Arne Brüsch, Stephan

Sigg, and Lars Wolf. “BANDANA – Body Area Network Device-to-device Authentication using Natural gAit”. In: IEEE International Conference on Pervasive Computing and Communications (PerCom). Mar. 2017, pp. 190–196

● Dominik Schürmann, Arne Brüsch, Ngu Nguyen, Stephan Sigg, and Lars Wolf. “Moves like Jagger: Exploiting variations in instantaneous gait for spontaneous device pairing”. Submitted to: Pervasive and Mobile Computing. 2018

● Arne Brüsch, Ngu Nguyen, Dominik Schürmann, Stephan Sigg, and Lars Wolf. “On the secrecy of publicly observable biometric features: security properties of gait for mobile device pairing”. Submitted to: IEEE Transactions on Mobile Computing (TMC). 2018

AcknowledgmentsWe appreciate partial funding from an EIT Digital HII Active project, Academy of Finland and the German Academic Exchange Service (DAAD).

Quantization Algorithm

Devices on the same body

Devices on different bodies

Osaka dataset: Intra-body (same body) and inter-body (different bodies) results

ComponentsBANDANA’s quantization algorithm compares instantaneous gait acceleration to the mean gait acceleration to derive binary gait fingerprints

Secure communication between smartwatch and smartphone

Implementation

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