TY - GEN
T1 - Crowdsourced measurements for device fingerprinting
AU - Andrews, Seth
AU - Gerdes, Ryan M.
AU - Li, Ming
N1 - Funding Information:
This work was partially supported by the National Science Foundation under grants CNS-1410000, CNS-1619728, and CNS-1731164
Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/5/15
Y1 - 2019/5/15
N2 - Physical layer identification allows verifying a user's identity based on their transmitter hardware. In contrast with digital identifiers at higher protocol layers, physical layer identification or device fingerprinting can identify unique signal characteristics at the physical layer introduced by manufacturing variability specific to each device. Recently, dynamic spectrum access has been proposed to allow a larger number of devices to efficiently access wireless spectrum. In such a system many low-cost devices may be distributed over a large area with spectrum allocated and managed by a central authority. Traditional authentication methods may not be secure, or adequate to identify existing users in a backwards compatible way: Identifiers such as MAC addresses can be impersonated, and the number of devices and their distributed nature may make key distribution and revocation difficult. Consequently, physical layer identification can be used to augment other security measures. We consider a crowdsourced scenario where individual users observe a signal using their own receiver and report their measurements to an enforcement authority which then identifies malicious users. Three types of measurements that can be crowdsourced are considered: actual signal observations, feature values, and fingerprinter output. Several methods for combining these measurements are considered. Performance is demonstrated on data collected from three wireless channels, used to simulate multiple receivers, from a total of twelve transmitters. The methods are evaluated in terms of required computational resources, bandwidth to report measurements, and how they are affected by mismatch in receiver characteristics. It is found that the crowdsourcing measurements can provide an improvement over individual receivers, with the best method dependent on the features and receivers used.
AB - Physical layer identification allows verifying a user's identity based on their transmitter hardware. In contrast with digital identifiers at higher protocol layers, physical layer identification or device fingerprinting can identify unique signal characteristics at the physical layer introduced by manufacturing variability specific to each device. Recently, dynamic spectrum access has been proposed to allow a larger number of devices to efficiently access wireless spectrum. In such a system many low-cost devices may be distributed over a large area with spectrum allocated and managed by a central authority. Traditional authentication methods may not be secure, or adequate to identify existing users in a backwards compatible way: Identifiers such as MAC addresses can be impersonated, and the number of devices and their distributed nature may make key distribution and revocation difficult. Consequently, physical layer identification can be used to augment other security measures. We consider a crowdsourced scenario where individual users observe a signal using their own receiver and report their measurements to an enforcement authority which then identifies malicious users. Three types of measurements that can be crowdsourced are considered: actual signal observations, feature values, and fingerprinter output. Several methods for combining these measurements are considered. Performance is demonstrated on data collected from three wireless channels, used to simulate multiple receivers, from a total of twelve transmitters. The methods are evaluated in terms of required computational resources, bandwidth to report measurements, and how they are affected by mismatch in receiver characteristics. It is found that the crowdsourcing measurements can provide an improvement over individual receivers, with the best method dependent on the features and receivers used.
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U2 - 10.1145/3317549.3323405
DO - 10.1145/3317549.3323405
M3 - Conference contribution
AN - SCOPUS:85066740426
T3 - WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks
SP - 72
EP - 83
BT - WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks
PB - Association for Computing Machinery, Inc
T2 - 12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2019
Y2 - 15 May 2019 through 17 May 2019
ER -