关于网络中入侵节点信息优化检测仿真研究

摘要

对网络中入侵节点信息进行优化检测,能够更好的保障网络安全稳定运行.对入侵节点信息检测时,需要根据节点的最佳获取路径,得到SVM的最优参数,来完成对入侵节点信息的检测.传统方法利用蚁群寻觅网络节点路径,得到支持向量机参数,但忽略了该参数的最优化,导致对信息的检测结果不准确.提出基于属性攻击图的入侵节点信息优化检测方法.定义网络潜质入侵的属性攻击图,将具有入侵节点信息的复杂入侵信号分解为IMF单频入侵信号,获取网络入侵检测系统的状态转移方程,将蚁群理论和支持向量机参数相融合,将网络入侵检查率作为目标函数,并对蚂蚁进行高斯异变,将最优路径上的节点连接起来得到SVM最优参数,以参数为依据完成对网络入侵节点信息检测.实验证明,所提方法设计精确度高,可以有效的提升嵌入式计算机网络入侵检测精度.%An optimization detection method of intrusion node information is proposed based on the attribute attack graph.Firstly,the attribute attack graph of network potential intrusion is defined,and the complex intrusion information with the intrusion node information is decomposed into the IMF single frequency intrusion signal to obtain the state transition equation of intrusion detection system.Then,the ant colony is integrated with the parameter of support vector machine,and the inspection rate of network intrusion is used as the objective function.Moreover,the Gaussian variation is made to the ant and the nodes on the optimal path are connected to obtain the SVM optimal parameter.Finally,the information detection of network intrusion node is completed according to the parameter.The experiment results show that the method has high design precision.It can improve the intrusion detection precision of embedded computer network.