基于码元包络幅值提取的网络入侵检测算法
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摘要
为了提高无线网络对移动终端高级持续性入侵检测的准确性,提出基于码元包络幅值提取的无线传感网络移动终端高级持续性入侵数据检测算法。构建无线传感网络的码元传输信道分布模型,提取无线传感网络传输流量序列的码元包络幅值特征量,根据码元包络幅值特征提取结果进行融合聚类处理,采用大数据信息融合和关联规则挖掘方法进行无线传感网络移动终端高级持续性入侵检测。仿真结果表明,采用该方法进行无线传感网络移动终端高级持续性入侵检测的准确性较高,抗干扰性较好,提高了网络的安全性。
In order to improve the accuracy of wireless network for mobile terminal advanced persistent intrusion detection,a network intrusion detection algorithm based on symbol envelope amplitude extraction is proposed. An advanced persistent intrusion detection algorithm for mobile terminals in wireless sensor networks based on symbol envelope amplitude extraction is proposed. The symbol transmission channel distribution model of wireless sensor network is constructed, the symbol envelope amplitude characteristic quantity of wireless sensor network transmission symbol is extracted,and the fusion clustering processing is carried out according to the result of symbol envelope amplitude feature extraction. Big data information fusion and association rule mining methods are used to detect advanced persistent intrusion of wireless sensor network mobile terminal. The simulation results show that the proposed method has high accuracy and good anti-interference,and improves the security of the wireless sensor network.
In order to improve the accuracy of wireless network for mobile terminal advanced persistent intrusion detection,a network intrusion detection algorithm based on symbol envelope amplitude extraction is proposed. An advanced persistent intrusion detection algorithm for mobile terminals in wireless sensor networks based on symbol envelope amplitude extraction is proposed. The symbol transmission channel distribution model of wireless sensor network is constructed, the symbol envelope amplitude characteristic quantity of wireless sensor network transmission symbol is extracted,and the fusion clustering processing is carried out according to the result of symbol envelope amplitude feature extraction. Big data information fusion and association rule mining methods are used to detect advanced persistent intrusion of wireless sensor network mobile terminal. The simulation results show that the proposed method has high accuracy and good anti-interference,and improves the security of the wireless sensor network.
引文
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[3]高妮,贺毅岳,高岭.海量数据环境下用于入侵检测的深度学习方法[J].计算机应用研究,2018,35(4):1197-1200.
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[6]任维武,张波辰,底晓强,等.基于人工蜂群优化的密度聚类异常入侵检测算法[J].吉林大学学报(理学版),2018,56(1):95-100.
[7]GENG Zhe,DENG Hai,HIMED B.Adaptive radar beamforming for interference mitigation in radar-wireless spectrum sharing[J].IEEE Signal Processing Letters,2015,22(4):484-488.
[8]YANG Yunchuan,SUN Cong,ZHAO Hui,et al.Algorithms for secrecy guarantee with null space beamforming in two-way relay networks[J].IEEE Transactions on Signal Processing,2014,62(8):2111-2126.
[9]郭华平,董亚东,毛海涛,等.一种基于逻辑判别式的稀有类分类方法[J].小型微型计算机系统,2016,37(1):140-145.
[10]XU Li,JIA Jiaya,MATSUSHITA Y.Motion detail preserving optical flowestimation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(9):1744-1757.
[11]LIU Zhi,ZHANG Xiang,LUO Shuhua,et al.Superpixel-based spatiotemporal saliency detection[J].IEEE Transactions on Circuits and Systems for Video Technology,2014,24(9):1522-1540.
[12]张红蕊,张永,于静雯.云计算环境下基于朴素贝叶斯的数据分类[J].计算机应用与软件,2015,32(3):27-30.
[13]吴志军,李光,岳猛.基于信号互相关的低速率拒绝服务攻击检测方法[J].电子学报,2014,42(9):1760-1766.
[14]张博,郝杰,马刚,等.混合概率典型相关性分析[J].计算机研究与发展,2015,52(7):1463-1476.
[15]贾昊,董泽,闫来清.基于信号分解和统计假设检验的稳态检测方法[J].仪器仪表学报,2018,39(10):150-157.
[16]蒋帅,孙小凡,向茂生,等.一种基于DEM的机载干涉相位生成算法[J].国外电子测量技术,2018,37(9):56-61.