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BANDANA Body Area Network Device-to-device Authentication using - - PowerPoint PPT Presentation

BANDANA Body Area Network Device-to-device Authentication using Natural gAit Dominik Schrmann * , Arne Brsch * , Stephan Sigg , Lars Wolf * , 2017-03-15 * Institute of Operating Systems and Computer Networks, TU Braunschweig Ambient


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BANDANA

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

Dominik Schürmann*, Arne Brüsch*, Stephan Sigg†, Lars Wolf*, 2017-03-15

*Institute of Operating Systems and Computer Networks, TU Braunschweig †Ambient Intelligence, Comnet, Aalto University

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Introduction Quantization Protocol Conclusion

Waking up on Hawaii…

Jet lag Awesome sunrise Let’s go jogging

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 2 of 14

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Introduction Quantization Protocol Conclusion

Waking up on Hawaii…

Quantified self

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 2 of 14

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Introduction Quantization Protocol Conclusion

Waking up on Hawaii…

Putting on your wearables

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 2 of 14

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Introduction Quantization Protocol Conclusion

Waking up on Hawaii…

Putting on all your wearables

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 2 of 14

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Introduction Quantization Protocol Conclusion

Waking up on Hawaii…

Putting on all your wearables

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 2 of 14

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Introduction Quantization Protocol Conclusion

Device-to-Device Authentication

Bluetooth Authentication

“Just works” profile Still pressing buttons DH key exchange No MitM protection

2017-03-15 Dominik Schürmann, Arne Brüsch, Stephan Sigg, Lars Wolf BANDANA Page 3 of 14

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Introduction Quantization Protocol Conclusion

Device-to-Device Authentication

Bluetooth Authentication

“Just works” profile Still pressing buttons DH key exchange No MitM protection

BANDANA

Person’s gait (walking pattern) Zero interaction Independent of on-body location MitM protection

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Introduction Quantization Protocol Conclusion

Novelty

Unlock smartphones

Muaaz et al. 2015 Hoang et al. 2015

Device2Device Authentication

No init procedure with templates Fresh secrets for each D2D authentication

Gait Cycle

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Introduction Quantization Protocol Conclusion

Accelerometer Reading

1 2 3 4 5 6 7 −5 5 Time [s] Acceleration [m/s2] Accelerometer reading on z-axis only

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Introduction Quantization Protocol Conclusion

Rotated Signal

1 2 3 4 5 6 7 10 20 Time [s] Acceleration [m/s2] Orientation relative to ground using Madgwick’s Algorithm

Notice influence of gravity g

z y x g

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Introduction Quantization Protocol Conclusion

Noise-Reduced Signal

1 2 3 4 5 6 7 −5 5 Time [s] Acceleration [m/s2] Apply a bandpass filter to keep frequencies between 0.5 and 12 Hz

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Introduction Quantization Protocol Conclusion

Gait-Cycle Detection

1 2 3 4 5 6 −5 5 Time [s] Acceleration [m/s2] Partition data into gait cycles Resample gait cycles to equal length Calculate average gait cycle

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Introduction Quantization Protocol Conclusion

Quantization

−5 5 Acceleration [m/s2] −5 5 Acceleration [m/s2] −5 5 Acceleration [m/s2]

Cycle Average Cycle 1 0 0 1 Average gait cycle overlaid on each original gait cycle 4 bits per cycle

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Introduction Quantization Protocol Conclusion

Quantization

−5 5 Acceleration [m/s2]

a) 1001 0100 1001 1010 1010 1001 0101 0110 b) 1001 0100 1001 1010 1010 1001 0101 0110 c) 0111 1000 1001 0101 1000 1100 1011 1000

Average gait cycle overlaid on each original gait cycle 4 bits per cycle

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Introduction Quantization Protocol Conclusion

Comparison between Locations

−5 5 Acceleration [m/s2] forearm: 0111

1000 1001 0101 1000 1100 1011 1000

−5 5 Acceleration [m/s2] waist: 0110

1000 1001 0001 1001 1001 1100 1010

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Introduction Quantization Protocol Conclusion

Evaluation

I n t r a

  • b
  • d

y c h e s t f

  • r

e a r m h e a d s h i n t h i g h u p p e r a r m w a i s t 0.2 0.4 0.6 0.8 1 Inter-body Similarity

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Introduction Quantization Protocol Conclusion

A A A A A A B B B B B B Authentication Request Sensor Recording Madgwick, Bandpass Filter Gait Cycle Detection Quantization, Reliability ⇒ ˜ fA, rA Sensor Recording Madgwick, Bandpass Filter Gait Cycle Detection Quantization, Reliability ⇒ ˜ fB, rB r

A

r

B

If h(rB) > h(rA): rA = rB fA = Rel(˜ fA, rA) fA

Fuzzy Crypto

− − − − − − → k If h(rA) > h(rB): rB = rA fB = Rel(˜ fB, rB) fB

Fuzzy Crypto

− − − − − − → k Password Authenticated Key Exchange (PAKE) s = PAKE(k) s = PAKE(k)

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Introduction Quantization Protocol Conclusion

Conclusion

Device-to-Device authentication for Body Area Networks Zero-interaction based on human gait pattern For 128 bit keys, 192 bit fingerprints are generated (48 cycles), disregarding 64 unreliable bits Worst-case duration: 96 s 80 % similarity required for fuzzy cryptography ⇒ 103-bit security level for the PAKE password.

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Introduction Quantization Protocol Conclusion

Conclusion

Device-to-Device authentication for Body Area Networks Zero-interaction based on human gait pattern For 128 bit keys, 192 bit fingerprints are generated (48 cycles), disregarding 64 unreliable bits Worst-case duration: 96 s 80 % similarity required for fuzzy cryptography ⇒ 103-bit security level for the PAKE password. Any questions? Dominik Schürmann <schuermann@ibr.cs.tu-bs.de>

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Backup Slides

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Spectral Coherence

5 10 15 20 25 0.2 0.4 0.6 0.8 1 Frequency γ2 Same subjects, different locations Different subjects, same locations Different subjects, different locations

Figure: Average spectral coherence over full sensor readings of the Mannheim dataset for same and different subject.

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Reliability

N + N + 1 6 N + 3 2 N + 4 8 N + 6 4 N + 1 2 8 0.6 0.7 0.8 0.9 1 Total Fingerprint Size M Similarity [%]

Figure: Fingerprint similarity of different sizes M with cutoff at N = 128 to evaluate the influence of Rel().

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Fingerprint Similarity

Table: Fingerprint similarity between locations on the same body (intra-body). Shown is the mean over all 15 subjects.

chest forearm head shin thigh upperarm waist chest 1.0 0.82 0.74 0.78 0.78 0.88 0.81 forearm 0.82 1.0 0.8 0.81 0.88 0.89 0.89 head 0.74 0.8 1.0 0.8 0.76 0.77 0.78 shin 0.78 0.81 0.8 1.0 0.77 0.78 0.8 thigh 0.78 0.88 0.76 0.77 1.0 0.85 0.84 upperarm 0.88 0.89 0.77 0.78 0.85 1.0 0.88 waist 0.81 0.89 0.78 0.8 0.84 0.88 1.0

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Entropy

1-19 20-35 36-47 48-51 52-55 56-88 89-95 0.2 0.4 0.6 0.8 1 p-value 1:birthdays 5:bitsream 9:count1sstr 13:3dsphere 17:marsagliatsangcd 36-47:rgb-bitdistribution (1-12) 90:dab-bytedistrib 2:operm5 6:opso 10:count1sbyt 14:squeeze 18:sts-monobit 48-51:rgb-minimum-distance (2-5) 91:dab-dct 3:rank32x32 7:oqso 11:parkinglot 15:runs 19:sts-runs 52-55:rgb-permutations (2-5) 92-93:dab-filltree (20-21) 4:rank6x8 8:dna 12:2dsphere 16:craps 20-35:sts-serial (1-16) 56-88:rgb-lagged-sum (0-32) 94:dab-filltree (32) 89:rgb-kstest-test 95:dab-monobit2 (12)

Figure: Distribution of p-values achieved for 128 bit keys (fingerprint length M = 192, 64 unreliable bits removed) in 21 runs of the various statistical tests of the dieHarder set of statistical tests.

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