基于多元节点属性分类的光纤网络入侵中未感染节点检测
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摘要
光纤网络采用开放性较强的分布式结构,易受到恶意数据和代码的入侵。提出基于多元节点属性分类的光纤网络入侵未感染节点检测算法研究。依据节点测距原理,提取光纤网络中全部节点的位置信息;选定与未感染节点类型相关的光纤节点特征属性;并针对节点属性和入侵类型建模。依据多元分类算法对提取的光纤节点样本空间采样特征数据进行学习和分类,检测光纤网络中的入侵未感染节点。仿真实验表明,提出的节点检测算法克服了传统算法的弊端和不足,能够有效降低通信成本和节点能耗、提高入侵检测率、延长光纤网络生命周期。
The optical fiber network adopts a strong open distributed structure,which is vulnerable to malicious data and code intrusion,and proposes a study on the detection algorithm of uninfected nodes based on the attribute classification of multiple nodes. According to the principle of node distance measurement,the location information of all nodes in the optical fiber network is extracted,the characteristic attributes of the optical fiber nodes related to the type of uninfected nodes are selected,and the node attributes and intrusion types are modeled,and the sampled spatial sampling features of the extracted optical fiber nodes are studied and classified according to the multiple classification algorithm. The intrusion of uninfected nodes in the optical fiber network. The simulation experiment shows that the proposed node detection algorithm overcomes the disadvantages and shortcomings of the traditional algorithm. It can effectively reduce the communication cost and energy consumption,improve the intrusion detection rate and prolong the life cycle of the optical fiber network.
The optical fiber network adopts a strong open distributed structure,which is vulnerable to malicious data and code intrusion,and proposes a study on the detection algorithm of uninfected nodes based on the attribute classification of multiple nodes. According to the principle of node distance measurement,the location information of all nodes in the optical fiber network is extracted,the characteristic attributes of the optical fiber nodes related to the type of uninfected nodes are selected,and the node attributes and intrusion types are modeled,and the sampled spatial sampling features of the extracted optical fiber nodes are studied and classified according to the multiple classification algorithm. The intrusion of uninfected nodes in the optical fiber network. The simulation experiment shows that the proposed node detection algorithm overcomes the disadvantages and shortcomings of the traditional algorithm. It can effectively reduce the communication cost and energy consumption,improve the intrusion detection rate and prolong the life cycle of the optical fiber network.
引文
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14 George S,Mamani N,Lucero Y,et al.Detection of helicobacter pylori by real-time PCR for 16s rRNA in stools of non Infected healthy children,using ELISA antigen stool test as the gold standard.Helicobacter,2016;21(6):606-612
15 Fang Z,Luo H Y,Quan L,et al.Node localization algorithm based on kernel function and Markov chains.Journal on Communications,2010;31(11):195-204
16周治平,邵楠楠.基于贝叶斯的改进WSNs信任评估模型.传感技术学报,2016;29(6):927-933Zhou Zhiping,Shao Nannan.An improved trust evaluation model based on Bayesian for WSNs.Chinese Journal of Sensors and Actuators,2016;29(6):927-933
17 Seo J W,Lee S J.A study on efficient detection of network-based IPspoofing DDo S and malware-infected systems.Springerplus,2016;5(1):1878-1880
18吴志芳,刘昕.无线网络传输数据节点入侵优化检测仿真.计算机仿真,2017;34(2):326-329Wu Zhifang,Liu Xin.Wireless network transmission data node intrusion detection simulation.Computer Simulation,2017;34(2):326-329
2何媛,谢林栩.光纤传感网络中入侵信号片段高精度分割技术研究.激光杂志,2017;38(6):93-96He Yuan,Xie Linyu.High precision segmentation of intrusion signal segments in optical fiber sensor networks.Laser Journal,2017;38(6):93-96
3宋瑞龙,吕宏伟.被入侵网络中的活跃节点检测方法研究.计算机仿真,2016;33(8):252-255Song Ruilong,Lv Hongwei.It was an invasion in active nodes in network detection method research.Computer Simulation,2016,33(8):252-255
4 Sun S,Chu F.Temperature compensation of fiber bragg grating current sensor based on optimized neural network algorithm.Acta Optica Sinica,2017;37(10):100-601
5张燕君,刘文哲,付兴虎.基于BSS和BPNN-DS算法的光纤入侵传感网络信息的高精度识别.光学学报,2016;32(1):32-40Zhang Yanjun,Liu Wenzhe,Fu Xinghu.High precision recognition of optical fiber intrusion sensing network information based on BSS and BPNN-DS algorithm.Acta Optica Sinica,2016;32(1):32-40
6杨芳,郭宏刚.网络入侵反追踪节点最优路径推演平台的设计与实现.现代电子技术,2016;39(22):14-17Yang Fang,Guo Honggang.Design and implementation of optimal path inference platform for network intrusion anti-tracking node.Modern Electronics Technique,2016;39(22):14-17
7胡艳.计算机网络入侵环境下健康节点通信选择算法的研究与仿真.微电子学与计算机,2017;34(1):141-144Hu Yan.Under the environment of network intrusion health node selection algorithm in the research and simulation of communication.Microelectronics&Computer,2017;34(1):141-144
8佟为明,梁建权,卢雷,等.基于节点信任值的WSNs入侵检测方案.系统工程与电子技术,2015;37(7):1644-1649Tong Weiming,Liang Jianquan,Lu Lei,et al.Intrusion detection scheme based node trust value in WSNs.Systems Engineering and Electronics,2015;37(7):1644-1649
9赵悦,程子傲,陈雷,等.移动Ad-Hoc网络叶节点簇降低能源节约入侵检测.控制工程,2016;23(7):1137-1142Zhao Yue,Cheng Zi'ao,Chen Lei,et al.Leaf node cluster reduce algorithm of mobile ad-hoc network based energy saving for intrusion detection.Control Engineering of China,2016;23(7):1137-1142
10李刚.基于自组织变异粒子滤波的网络入侵检测算法.科技通报,2015;31(12):102-103Li Gang.Network intrusion detection based on self-organization mutation particle filter algorithm.Bulletin of Science and Technology,2015;31(12):102-103
11曹洪星,白育堃,李敬辉,等.基于两种光纤介质模型的长周期光纤光栅传感特性分析.中国激光,2014;41(2):250-258Cao Hongxing,Bai Yukun,Li Jinghui,et al.Analysis of long period fiber grating sensing characteristics based on two kinds of optical fiber dielectric model.Chinese Journal of Lasers,2014;41(2):250-258
12睢丹,黄永灿.基于改进遗传算法的网络入侵检测.数字技术与应用,2016;25(4):191-192Sui Dan,Huang Yongcan.Network intrusion detection based on improved genetic algorithm.Digital Technology and Application,2016;25(4):191-192
13任帅,张引发,王鱼鲸,等.光网络中大功率信号引起的带内串扰攻击研究.半导体光电,2015;36(1):110-114Ren Shuai,Zhang Yinfa,Wang Yujing,et al.Research on intra-channel crosstalk attack caused by high power signals in optical networks.Semiconductor Optoelectronics,2015;36(1):110-114
14 George S,Mamani N,Lucero Y,et al.Detection of helicobacter pylori by real-time PCR for 16s rRNA in stools of non Infected healthy children,using ELISA antigen stool test as the gold standard.Helicobacter,2016;21(6):606-612
15 Fang Z,Luo H Y,Quan L,et al.Node localization algorithm based on kernel function and Markov chains.Journal on Communications,2010;31(11):195-204
16周治平,邵楠楠.基于贝叶斯的改进WSNs信任评估模型.传感技术学报,2016;29(6):927-933Zhou Zhiping,Shao Nannan.An improved trust evaluation model based on Bayesian for WSNs.Chinese Journal of Sensors and Actuators,2016;29(6):927-933
17 Seo J W,Lee S J.A study on efficient detection of network-based IPspoofing DDo S and malware-infected systems.Springerplus,2016;5(1):1878-1880
18吴志芳,刘昕.无线网络传输数据节点入侵优化检测仿真.计算机仿真,2017;34(2):326-329Wu Zhifang,Liu Xin.Wireless network transmission data node intrusion detection simulation.Computer Simulation,2017;34(2):326-329