摘要
针对当前网络入侵检测效率低的难题,提出了基于云计算技术的大规模网络入侵检测模型.首先收集网络入侵数据,并进行归一化处理,然后将整个数据集划分多个子集,采用云计算技术对每一个子集进行建模,最后采用KDD CUP数据集对模型的性能进行分析.结果表明,该模型加快了网络入侵检测的速度,可以满足大规模网络入侵在线检测要求.
In view of the low efficiency of network anomaly intrusion detection,a large-scale network intrusion detection model based on cloud computing technology is proposed.Firstly,network intrusion data are collected and normalized,and secondly data set are divide into multiple sub sets,and cloud computing is used to model each sub set.Finally,KDD CUP data set is used to analyze the performance of the model.The results show that the proposed model can speed up the network intrusion detection,and can meet the requirements of large-scale network intrusion detection in the linear.
出处
《内蒙古师范大学学报(自然科学汉文版)》
CAS
北大核心
2017年第6期884-887,892,共5页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
河北省人力资源和社会保障厅项目(JRS-2014-1106)
关键词
云计算技术
大规模数据
网络入侵检测
神经网络
cloud computing technology
large-scale data
network intrusion detection
neural network