TeRFF: Temperature-aware Radio Frequency Fingerprinting for Smartphones

In recent years, radio frequency (RF) fingerprinting has attracted more and more attention. Many different types of RF fingerprints have been proposed, such as carrier frequency offset (CFO), sampling frequency offset and error vector magnitude. Among them, the CFO fingerprint is recognized as a promising RF fingerprint. However, for commonly used smartphones, we find that its CFO fingerprint is unstable, because the temperature of crystal oscillator varies greatly and large fluctuations of temperature significantly affect its CFO fingerprint. Therefore, the solutions of CFO-based fingerprinting will no longer be effective for smartphones if the temperature of crystal oscillator is not involved. To this end, we propose a more reliable and applicable CFO-based fingerprinting approach called temperature-aware radio frequency fingerprinting (TeRFF). First, we construct a dataset by extracting crystal oscillator's temperature and the corresponding CFO value on multiple smartphones over a period. In the dataset, the extracted temperature values constitute a set of temperature values, and each registered temperature value corresponds to a group of CFO samples. On this basis, we train multiple Naive Hayes models, each tagged with a registered temperature value. Moreover, since there are many temperature values which are not in the temperature set, we design a CFO estimation method to estimate the CFO fingerprint at the unregistered temperature. Finally, the experimental results demonstrate that our proposed solution TeRFF makes the CFO fingerprinting still efictive for smartphone identification, and its performance is better than other existing RF fingerprinting schemes.

Automated summary

In this study, the instability of the carrier frequency offset (CFO) fingerprint in commonly used smartphones due to temperature fluctuations is addressed. A more reliable and applicable temperature-aware radio frequency fingerprinting (TeRFF) approach is proposed, which involves constructing a dataset by extracting the temperature and corresponding CFO value from multiple smartphones. Multiple Naive Bayes models are trained based on registered temperature values, and a CFO estimation method is designed for unregistered temperatures. Experimental results demonstrate that TeRFF is an effective solution for smartphone identification, outperforming other existing RF fingerprinting schemes.