基于联合压缩感知重构的网络通信服务目标数据检测技术

Target Data Detection Technology of Network Communication Service Based on Joint Compressed Sensing Reconstruction

摘要:

采用当前无监督组件支持向量机模型检测技术、高维随机矩阵检测技术，对网络通信服务目标数据检测时，缺少特殊属性目标采集过程，导致数据检测效果较差。针对该问题，提出了基于联合压缩感知重构的网络通信服务目标数据检测技术研究。根据联合压缩感知重构原理，采集网络通信服务节点温度稀疏目标数据，利用联合压缩感知重构技术处理网络节点通信数据，构造稀疏二进制矩阵，完成对未知数量检测数据精确重构。利用构造函数计算网络通信服务数据之间的相似度，完成不同样本特征的区分，剔除不必要数据特征，并采用联合压缩感知重构技术实现对网络通信服务目标数据检测。实验结果表明，该技术数据检出率最高可达到87%，为海量数据下社交网络特殊对象数据挖掘奠定基础。

Abstract:

Abstract: The current unsupervised component support vector machine model detection technology and high-dimensional random matrix detection technology are used. When the target data of network communication service is detected, the special attribute target acquisition process is lacking, resulting in poor data detection. Aiming at this problem, this paper proposes a research on network communication service target data detection technology based on joint compressed sensing reconstruction. According to the principle of joint compressed sensing reconstruction, the temperature sparse target data of the network communication service node is collected, and the joint compressed sensing reconstruction technology is used to process the communication data of the network node, and the sparse binary matrix is ??constructed to complete the accurate reconstruction of the unknown quantity detection data. The constructor is used to calculate the similarity between network communication service data, to distinguish different sample features, to eliminate unnecessary data features, and to use joint compressed sensing reconstruction technology to detect network communication service target data. The experimental results show that the detection rate of the technology data can reach 87%, which lays a foundation for the data mining of social network special objects under massive data.