基于多维特征的通信网络异常数据识别算法

Algorithm for Identifying Abnormal Data in Communication Networks Based on Multidimensional Features

为解决现有方法存在的异常数据识别精度较低的问题,提出一种基于多维特征的通信网络异常数据识别算法。调整粒子群优化算法中粒子的当前速度和位置,获取通信网络多维数据样本;通过数据挖掘中的聚类分析法提取数据特征,确定密度指标,获取数据多维特征;将提取的多维特征引入深度信念网络中进行识别,根据特征频谱幅值变化,实现对通信网络数据异常识别。实验结果表明,该算法能有效识别通信网络异常数据特征,具有较高的识别准确性。

To solve the problem of low accuracy in identifying abnormal data in existing methods.An abnormal data recognition algorithm for multi-dimensional feature-based communication network is proposed.The current speed and position of particles in particle swarm optimization algorithm is adjusted to obtain multi-dimensional data samples of communication network.Data features are extracted through clustering analysis in data mining,determining density indicators,and obtaining multidimensional features of the data.The extracted multidimensional features are Introduced into the deep belief network for recognition,and anomaly recognition of communication network data is achieved based on changes in feature spectrum amplitude.The experimental results show that the algorithm can effectively identify abnormal data features in communication networks and has high recognition accuracy.