IDE-SVM algorithm and it's usage in physical layer security method for IoT

Abstract

To address the problem that there is no standard for the parameter selection of support vector machine (SVM) algorithm, a parameter optimization selection method based on the integrated improved differential evolution (IDE) algorithm is proposed, which uses the minimization of the classification error rate as the optimization criterion and the improved differential evolution algorithm to optimize the combination of SVM parameters to obtain an SVM algorithm with higher classification accuracy. At the same time, to avoid the basic DE algorithm from falling into local optimum when solving the SVM parameter selection problem and to improve the search efficiency of the DE algorithm. In this paper, an improved differential evolutionary algorithm is proposed to obtain a DE algorithm with faster convergence and higher accuracy by using the circular arc function for adaptive control of the variance scaling factor F and the crossover probability factor R, combining with the random newborn individual replacement operation. Based on this, an IDE-SVM IoT physical layer security method based on IDE-SVM is proposed. The experimental results show that the authentication accuracy of the physical layer security method based on the improved SVM algorithm is higher than others.

Keywords

• 物理层安全
• 支持向量机
• 差分进化
• 参数优化