Name
Abstract | ||
|---|---|---|
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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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3317549.3323405 | Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks |
Field | DocType | ISBN |
Computer science,Computer network,Human–computer interaction | Conference | 978-1-4503-6726-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seth Andrews | 1 | 0 | 0.68 |
| Ryan M. Gerdes | 2 | 41 | 12.72 |
| Ming Li | 3 | 1770 | 84.74 |