Semi-Supervised RF Fingerprinting with Consistency-Based Regularization
This work addresses a practical limitation in wireless authentication for security applications, but it is incremental as it combines existing techniques in a new domain.
The paper tackled the problem of limited labeled data in radio frequency fingerprinting for wireless security by proposing a semi-supervised deep learning method with data augmentation, consistency regularization, and pseudo-labeling. The result showed that it outperformed other methods and achieved performance close to fully supervised learning with very few labeled examples.
As a promising non-password authentication technology, radio frequency (RF) fingerprinting can greatly improve wireless security. Recent work has shown that RF fingerprinting based on deep learning can significantly outperform conventional approaches. The superiority, however, is mainly attributed to supervised learning using a large amount of labeled data, and it significantly degrades if only limited labeled data is available, making many existing algorithms lack practicability. Considering that it is often easier to obtain enough unlabeled data in practice with minimal resources, we leverage deep semi-supervised learning for RF fingerprinting, which largely relies on a composite data augmentation scheme designed for radio signals, combined with two popular techniques: consistency-based regularization and pseudo-labeling. Experimental results on both simulated and real-world datasets demonstrate that our proposed method for semi-supervised RF fingerprinting is far superior to other competing ones, and it can achieve remarkable performance almost close to that of fully supervised learning with a very limited number of examples.