摘要
针对传统计算机网络入侵监测方法存在监测率较低、监测时间长的问题,提出基于混沌粒子群优化(Particle Swarm optimization,Pso)的计算机网络入侵实时监测方法。初始化不同粒子的位置和速度,对群体中的最优粒子执行混沌局部搜索并更新群体最优值,构建网络入侵监测模型,设置网络入侵优化的适应度函数,运用混沌算法计算最大重复搜索次数,获取全局最优位置,实现实时监测。实验结果表明,运用该方法的入侵监测率较高,监测时间较短,具有一定的实用性。
Aiming at the problems of low monitoring rate and long monitoring time in traditional computer network intrusion monitoring methods,a real-time computer network intrusion monitoring method based on chaotic Particle Swarm Optimization(PSO)is proposed.Initialize the positions and speeds of different particles,perform chaotic local search on the best particles in the group and update the optimal value of the group,build a network intrusion monitoring model,set the fitness function of network intrusion optimization,use the chaotic algorithm to calculate the maximum number of repeated searches,obtain the optimal position of the whole bureau,and realize real-time monitoring.The experimental results show that the intrusion detection rate using this method is high,the monitoring time is short,and it has certain practicality.
作者
江雪姣
JIANG Xuejiao(Hunan Vocational College of Science and Technology,Yiyang Hunan 410004,China)
出处
《信息与电脑》
2023年第9期31-33,共3页
Information & Computer
关键词
粒子群优化(PSO)
网络入侵
监测
计算机
Particle Swarm Optimization(PSO)
network intrusion
monitoring
computer
