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首页> 外文期刊>Network Science and Engineering, IEEE Transactions on >Convolution Based Feature Extraction for Edge Computing Access Authentication
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Convolution Based Feature Extraction for Edge Computing Access Authentication

机译：基于卷积的边缘计算访问身份验证的特征提取

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摘要

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.
机译：在本文中，呈现了一种卷积神经网络（CNN）增强的射频指纹（RFF）认证方案用于物联网（物联网）。 RFF是一种非加密认证技术，通过在边缘服务器上处理接收的RF信号来识别通过RF瞬态信号的波形，这在不实现任何加密算法的情况下，将没有成本负担在不实现任何加密算法的情况下满足事物互联网上实时访问身份验证的需求。在新方案中，讨论了基于一维（1D）信号卷积的提取特征的可行性，参考来自CNN的特征的方法，并与信号卷积的特性组合。 1D信号的卷积内核旨在提取信号的特征，以减少培训时间并确保分类准确性。因此，与这些传统算法相比，它可以提高准确性，同时将更新参数的训练时间重复作为神经网络。在真实信号采集系统中验证了ThealGorithm的准确性和训练时间。结果证明，新颖的算法可以有效地提高低信噪比（SNR）的分类精度，同时将训练时间保持在可接受的范围内。

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