摘要
常规的通信网络流量异常监测方法在监测效率方面无法较好地满足通信网络流量监测的精度需求,为此提出基于粒子群优化(Particle Swarm Optimization,PSO)算法的通信网络流量异常智能监测方法。首先,根据通信网络流量特征构建经验库,利用PSO算法优化通信网络粒子群;其次,寻找全局最优解,通过分级的智能监测模式获取流量异常智能监测结果;最后,进行实验对比分析。实验结果表明,该方法的网络流量异常智能监测准确率较高,均达到96%以上,满足通信网络流量监测精度方面的需求。
Conventional communication network traffic anomaly monitoring methods cannot meet the accuracy requirements of communication network traffic monitoring in terms of monitoring efficiency.Therefore,an intelligent communication network traffic anomaly monitoring method based on Particle Swarm Optimization(PSO)algorithm is proposed.Firstly,an experience base is constructed based on the traffic characteristics of the communication network,and the PSO algorithm is used to optimize the communication network particle swarm.Secondly,search for the global optimal solution and obtain intelligent monitoring results for traffic anomalies through a hierarchical intelligent monitoring mode.Finally,conduct experimental comparative analysis.The experimental results show that the intelligent monitoring accuracy of network traffic anomalies using this method is high,reaching over 96%,which meets the accuracy requirements of communication network traffic monitoring.
作者
张玲玲
ZHANG Linging(Jiangsu Sucheng Vocational School,Suqian Jiangsu 223800,China)
出处
《信息与电脑》
2023年第10期88-90,共3页
Information & Computer
关键词
粒子群优化(PSO)
通信流量
网络监测
网络粒子群
Particle Swarm Optimization(PSO)
communication traffic
network monitoring
network particle swarm optimization
