Convolution Based Feature Extraction for Edge Computing Access Authentication

Abstract

In this article, a convolutional neural network (CNN) enhanced radio frequency fingerprinting (RFF) authentication scheme is presented for Internet of things (IoT). RFF is a non-cryptographic authentication technology, identifies devices through the waveforms of the RF transient signals by processing received RF signals on the edge server, which places no cost burden to low-end (low-cost) devices without implementing any encryption algorithm and meet the demands of the real-time access authentication in Internet of things. In the new scheme, the feasibility of extracting features based on one-dimensional (1D) signal convolution is discussed, referring to the method of extracting features from CNN, and combining with the characteristics of signal convolution. A convolution kernel for 1D signals is designed to extract the feature of signals in order to reduce training time and ensure classification accuracy. Therefore, it can improve the accuracy compared with these traditional algorithms, while saving the training time of updating parameters repeatedly as the neural network. The accuracy and training time of thealgorithm are verified in a real signal acquisition system. The results prove that the novel algorithm can effectively improve the classification accuracy in low signal-to-noise ratio (SNR), while keeps the training time in an acceptable range.