Finding a 'New' Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning

Andrey Gritsenko, Zifeng Wang, Tong Jian, Jennifer G. Dy, Kaushik R. Chowdhury, Stratis Ioannidis

Abstract: Radio frequency fingerprinting enhances security and privacy of wireless networks and communications by learning and extracting unique characteristics embedded in transmitted signals. Deep learning-based approaches learn radio fingerprints without hand-engineering features. One persisting drawback in deep learning methods is they identify only devices that are previously observed in a training set: if a radio signal from a new, unseen, device is passed through the classifier, the source device will be classified as one of the known devices. We propose a novel approach that facilitates new class detection without retraining a neural network, and perform extensive analysis of the proposed model both in terms of model parameters and real-world datasets. We accomplish this by first breaking down a longer transmission burst into smaller slices, and assessing classifier confidence on a new transmission based on per slice statistics: our approach detects a new device with 76% accuracy, while reducing the classification accuracy of 500 previously seen devices by no more than 10%.