Deep Open Set Identification for RF Devices

TL;DR

This paper introduces a novel open set RF device identification method using CNNs and OpenMax to classify unseen device classes, enhancing security and robustness in IoT device authentication.

Contribution

It proposes a new open set classification framework for RF device identification combining CNN with STFT preprocessing and OpenMax for unknown class detection.

Findings

Outperforms existing methods in robustness and accuracy

Effective in recognizing unseen RF device classes

Validated on sampled data and voice signal sets

Abstract

Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process. However, existing approaches rely on the assumption that we can learn all the classes from the training set, namely, closed-set classification. To overcome the closed-set limitation, we propose a novel open set RF device identification method to classify unseen classes in the testing set. First, we design a specific convolution neural network (CNN) with a short-time Fourier transforming (STFT) pre-processing module, which efficiently recognizes the differences of feature maps learned from various RF device signals. Then to generate a representation of known class bounds, we estimate the probability map of the open-set via the OpenMax function. We conduct experiments on sampled data and voice signal sets, considering various pre-processing schemes, network structures, distance metrics, tail sizes, and openness degrees. The simulation results show the superiority of the proposed method in terms of robustness and accuracy.