特价之三

Title：

Feature extraction method of abnormal communications in Blockchain environment

（区块链环境下异常通信特征提取方法）

Abstract：

With the advent of pervasive, dependable, and near-instant wireless connectivity for humans and machines, Blockchain technology will serve as the backbone of society's digital revolution. For ensuring successful communication in the Blockchain environment, an accurate extraction of abnormal communication features is imperative and innovative mechanisms are required to achieve the seamless communication. In order to achieve this objective, a nonlinear technology-based feature extraction method for anomalous communication signals is proposed in this article. In the proposed technique, wavelet transformation is employed to deconstruct the aberrant network communication signals in high and low frequency bands. The corresponding parameters for phase space reconstruction and nonlinear dimensionality reduction of the local tangent space mainstream shape identification method are chosen based on the distribution features of the signal and noise in the frequency range. Using the KPCA approach (which combines principal component analysis and kernel learning), the wavelet packet decomposition coefficients are recreated after noise reduction to achieve nonlinear noise of abnormal signals. The high-dimensional feature space is mapped to the de-noised abnormal communication signal in heterogeneous networks that connect distributed blockchain databases. The principal component is analyzed according to the nonlinear function in the mapped feature space, and the nonlinear function is solved by a self-organizing neural network to map the output to the principal component extraction result. The results reveal that the proposed strategy reduces signal noise effectively.

期刊：

IET Software

期刊分类—计算机：软件工程

国际刊号：1751-8806

分区：JCR 4区/中科院4区

影响因子：1.15

检索信息：SCIE

出版版面：专刊

出版社：Wiley