关于网络通信信息传输安全性能检测仿真
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摘要
对网络通信信息的安全性问题的研究,能够有效改善网络通信过程易受攻击现象。对通信信息传输安全性能进行检测,需要先对隐蔽异常流量进行提取,计算网络通信流量信号的瞬时参数,完成通信信息传输过程安全性能的提升。传统方法采用正向与逆向云产生方法,获得待测网络环境下不同异常等级的流量评估云,对异常通信信息流量进行检测,但忽略了获取通信流量信号的瞬时参数,导致检测精度偏低。提出网络通信隐蔽流量异常检测方法。建立网络通信流量传输结构模型对流量进行时频采样和时间序列分析,进行网络通信流量抗干扰滤波预处理,计算网络通信流量信号的瞬时参数,并以参数计算出流量信号全局异常子空间,将获得全局相关系数对流量是否异常进行判定。实验结果表明,上述方法能够检测出网络多条链路上较小的流量异常,实现了通信信息传输安全性的高精度检测。
The research on the security of network communication information can effectively improve the network communication process.In order to detect the security of communication information transmission,we need extract the hidden abnormal traffic and calculate the instantaneous parameter.Traditional methods use forward and reverse cloud method to detect the abnormal communication information flow,which ignore the instantaneous parameter.This article presents a method for detecting hidden abnormal traffic of network communication.The transmission structure model of network communication traffic for time frequency sampling and time series analysis is built.Then,the anti-interference filter of network communication traffic is preconditioned and instantaneous parameter of network traffic signal is calculated.Finally,global abnormal subspace of flow signal through parameters is calculated,thus the abnormal flow is determined.Simulation results show that the above method can detect small traffic anomaly on multiple links of network with high precision.
The research on the security of network communication information can effectively improve the network communication process.In order to detect the security of communication information transmission,we need extract the hidden abnormal traffic and calculate the instantaneous parameter.Traditional methods use forward and reverse cloud method to detect the abnormal communication information flow,which ignore the instantaneous parameter.This article presents a method for detecting hidden abnormal traffic of network communication.The transmission structure model of network communication traffic for time frequency sampling and time series analysis is built.Then,the anti-interference filter of network communication traffic is preconditioned and instantaneous parameter of network traffic signal is calculated.Finally,global abnormal subspace of flow signal through parameters is calculated,thus the abnormal flow is determined.Simulation results show that the above method can detect small traffic anomaly on multiple links of network with high precision.
引文
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[10]危美林,等.面向异常流量检测的自适应抽样算法研究[J].计算机应用研究,2015,32(10):3052-3055.
[2]王铭鑫,等.一种SDN中基于熵值计算的异常流量检测方法[J].电信科学,2015,31(9):83-89.
[3]李宇翀,等.RMPCM:一种基于健壮多元概率校准模型的全网络异常检测方法[J].通信学报,2015,36(11):201-212.
[4]王端理.网络数据库中异常数据检测优化仿真[J].计算机仿真,2017,34(5):410-413.
[5]曹杰,殷保群,钱叶魁.基于流特性的网络流量异常检测研究[J].系统科学与数学,2015,35(10):1127-1134.
[6]费金龙,等.基于云模型的网络异常流量检测[J].计算机工程,2017,43(1):178-182.
[7]彭远浩,潘久辉.基于日志分析的增量数据捕获方法研究[J].计算机工程,2015,41(6):56-60.
[8]罗玲,殷保群,曹杰.基于sketch数据结构与正则性分布的骨干网流量异常分析与识别[J].系统科学与数学,2015,35(1):1-8.
[9]李宇翀,等.基于多元增量分析的全网络在线异常检测方法[J].上海交通大学学报,2016,50(9):1368-1375.
[10]危美林,等.面向异常流量检测的自适应抽样算法研究[J].计算机应用研究,2015,32(10):3052-3055.