基于人工蜂群算法及多变量决策的网络入侵检测方法研究

Research on Nnetwork Intrusion Ddetection Method Based on Artificial Bee Colony Algorithm and Multivariable Decision Making

传统的网络入侵检测方法存在对入侵信息无法准确识别的问题,为此引进人工蜂群算法及多变量决策技术,设计一种网络入侵检测方法。使用算法,对网络入侵特征进行识别,根据输出的特征,融合多变量决策,对网络入侵特征进行分类处理,以此检测并输出混合式异常IDS网络入侵集合,完成对检测方法的设计。此外,通过设计对比实验,将设计方法与传统方法进行对比,证明设计的检测方法,在实际应用中,可以准确地识别多组入侵网络的风险数据,相比传统方法检出率更高。

In order to solve the problem that traditional network intrusion detection methods can not accurately identify the intrusion information,this paper introduces artificial bee colony algorithm and multi-variable decision technology,and designs a network intrusion detection method.The Algorithm is used to identify the network intrusion features,and according to the output features,the multi-variable decision is fused to classify the network intrusion features,so as to detect and output the hybrid anomaly IDS network intrusion set,complete the design of the detection method.In addition,through the design contrast experiment,the design method is compared with the traditional method,which proves that the designed detection method can accurately identify the risk data of multi-group intrusion network in practical application,and the detection rate is higher than the traditional method.