Abstract

During the period of COVID-19 protection, Internet of Things (IoT) has been widely used to fight the outbreak of pandemic. However, the security is a major issue of IoT. In this research, a new algorithm knn-bp is proposed by combining BP neural network and KNN. Knn-bp algorithm first predicts the collected sensor data. After the forecast is completed, the results are filtered. Compared with the data screened by traditional BP neural network, k-nearest-neighbor algorithm has good data stability in adjusting and supplementing outliers, and improves the accuracy of prediction model. This method has the advantages of high efficiency and small mean square error. The application of this method has certain reference value. Knn-bp algorithm greatly improves the accuracy and efficiency of the Internet of things. Internet of things network security is guaranteed. It plays an indelible role in the protection of COVID-19.

中文翻译：

---

基于非对称加密算法和神经网络的COVID-19物联网安全设计与应用

摘要

在COVID-19保护期间，物联网（IoT）已被广泛用于抵抗大流行的爆发。但是，安全性是物联网的主要问题。结合BP神经网络和KNN，提出了一种新的算法knn-bp。Knn-bp算法首先预测收集到的传感器数据。预测完成后，将过滤结果。与传统的BP神经网络筛选的数据相比，k近邻算法在调整和补充异常值方面具有良好的数据稳定性，并提高了预测模型的准确性。该方法效率高，均方误差小。该方法的应用具有一定的参考价值。Knn-bp算法极大地提高了物联网的准确性和效率。物联网的网络安全得到保证。它在保护COVID-19中起着不可磨灭的作用。