Deep Open Set Identification for RF Devices

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....

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Bibliographic Details
Published in:arXiv.org 2021-12
Main Authors: Wang, Qing, Liu, Qing, Zhang, Zihao, Fang, Haoyu, Zheng, Xi
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Feature maps
Identification methods
Internet of Things
Signal processing
Online Access:Get full text
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Summary: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.
ISSN:2331-8422