基于时序信息分析的WSN贝叶斯信誉评价模型
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摘要
为了有效降低信道占用对节点信誉评价的影响,提高信誉评价模型的准确性,针对数据中断攻击和选择性转发攻击,结合信道状态对网络的影响,引入节点行为时间序列和信道状态时间序列,提出了基于时序信息分析的TS-BRS信誉模型.采用时序分析法,对两条时间序列匹配分析,降低信道冲突对信誉评价模型的干扰,提高模型识别的准确性;并在信誉值更新中引入适应性维护函数μ,加重现阶段节点行为对信誉值的影响,提高评价模型的适应性.仿真实验表明,新的信誉评价模型能有效提升模型的检测率和检测速度.引入维护函数,网络中被捕获的恶意节点的信誉值可以更快收敛.
In order to effectively reduce the influence of channel occupancy on the reputation evaluation of nodes,and to improve the accuracy of the reputation model,to tackle the data interrupt attacks and selective forwarding attacks,a TS-BRS reputation model was presented based on time series information analysis to evaluate the behavior of nodes.Considering the influence of channel state on network node behavior time series and channel state time series.And the adaptive maintenance function μ was also introduced to update reputation value,add the influence of node behavior on reputation value in reappearing stage,and improve the adaptability of evaluation model.The simulation results showed that the new reputation evaluation model could effectively improve the detection rate and detection speed for malicious nodes.The reputation value of a malicious node could converge more quickly.
In order to effectively reduce the influence of channel occupancy on the reputation evaluation of nodes,and to improve the accuracy of the reputation model,to tackle the data interrupt attacks and selective forwarding attacks,a TS-BRS reputation model was presented based on time series information analysis to evaluate the behavior of nodes.Considering the influence of channel state on network node behavior time series and channel state time series.And the adaptive maintenance function μ was also introduced to update reputation value,add the influence of node behavior on reputation value in reappearing stage,and improve the adaptability of evaluation model.The simulation results showed that the new reputation evaluation model could effectively improve the detection rate and detection speed for malicious nodes.The reputation value of a malicious node could converge more quickly.
引文
[1]MOOSAVI H,BUI F M.A game-theoretic framework for robust optimal intrusion detection in wireless sensor networks[J].Information forensics&security IEEEtransactions on,2014,9(9):1367-1379.
[2]李建坡,钟鑫鑫,徐纯.无线传感器网络静态节点定位算法综述[J].东北电力大学学报,2015,35(2):52-58.
[3]阎新芳,严晶晶,冯岩.WSN中基于梯度和粒子群优化算法的分级簇算法[J].郑州大学学报(工学版),2016,37(2):33-36.
[4]GANERIWAL S,SRIVASTAVA M B.Reputationbased framework for high integrity sensor networks[C]//Proceeding of the 2nd ACM Workshop on Security of ADHoc and Sensor Networks.New York:ACM,2004:66-77.
[5]盛燕.无线传感器网络恶意节点识别技术研究[D].哈尔滨工程大学信息与通信工程学院,2008.
[6]杨光,印桂生,杨武.无线传感器网络安全路由算法的研究与设计[J].计算机科学,2008,35(5):55-59.
[7]陈志奎,訾冰洁,姜国海,等.基于信任云的无线传感器网络信任评估[J].计算机应用,2010,30(12):3346-3348.
[8]OUYANG X,TIAN B,LI D,et al.A novel hierarchical reputation model for wireless sensor networks[J].International journal of digital content technology and its applications,2012,6(10):61-69.
[9]OUYAN X,LI D,ZHANG J Y,et al.Malicious node detection in wireless sensor networks using time series analysis on node reputation[J].Journal of convergence information technology,2012,7(15):8-16.
[10]崔慧,潘巨龙,闫丹丹.无线传感器网络中基于安全数据融合的恶意节点检测[J].传感技术学报,2014,27(5):664-669.
[11]韩挺,罗守山,辛阳,等.基于动态邻接信任模型的安全路由算法研究[J].通信学报,2013,34(6):191-200.
[12]邬春明,杨文月,程亮.基于Zig Bee的智能家居温湿度监测系统设计[J].东北电力大学学报,2012,32(4):14-17.
[13]阎新芳,张晓丹,严晶晶,等.WSN中基于离散人工鱼群的分簇拓扑优化算法[J].郑州大学学报(工学版),2017,38(4):69-72.
[2]李建坡,钟鑫鑫,徐纯.无线传感器网络静态节点定位算法综述[J].东北电力大学学报,2015,35(2):52-58.
[3]阎新芳,严晶晶,冯岩.WSN中基于梯度和粒子群优化算法的分级簇算法[J].郑州大学学报(工学版),2016,37(2):33-36.
[4]GANERIWAL S,SRIVASTAVA M B.Reputationbased framework for high integrity sensor networks[C]//Proceeding of the 2nd ACM Workshop on Security of ADHoc and Sensor Networks.New York:ACM,2004:66-77.
[5]盛燕.无线传感器网络恶意节点识别技术研究[D].哈尔滨工程大学信息与通信工程学院,2008.
[6]杨光,印桂生,杨武.无线传感器网络安全路由算法的研究与设计[J].计算机科学,2008,35(5):55-59.
[7]陈志奎,訾冰洁,姜国海,等.基于信任云的无线传感器网络信任评估[J].计算机应用,2010,30(12):3346-3348.
[8]OUYANG X,TIAN B,LI D,et al.A novel hierarchical reputation model for wireless sensor networks[J].International journal of digital content technology and its applications,2012,6(10):61-69.
[9]OUYAN X,LI D,ZHANG J Y,et al.Malicious node detection in wireless sensor networks using time series analysis on node reputation[J].Journal of convergence information technology,2012,7(15):8-16.
[10]崔慧,潘巨龙,闫丹丹.无线传感器网络中基于安全数据融合的恶意节点检测[J].传感技术学报,2014,27(5):664-669.
[11]韩挺,罗守山,辛阳,等.基于动态邻接信任模型的安全路由算法研究[J].通信学报,2013,34(6):191-200.
[12]邬春明,杨文月,程亮.基于Zig Bee的智能家居温湿度监测系统设计[J].东北电力大学学报,2012,32(4):14-17.
[13]阎新芳,张晓丹,严晶晶,等.WSN中基于离散人工鱼群的分簇拓扑优化算法[J].郑州大学学报(工学版),2017,38(4):69-72.