[1]滕志军,郭力文,吕金玲,等.基于时序信息分析的WSN贝叶斯信誉评价模型[J].郑州大学学报(工学版),2019,40(01):38.
 WSN Bayes Reputation Evaluation Model Based on Time Series Information Analysis[J].Journal of Zhengzhou University (Engineering Science),2019,40(01):38.
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基于时序信息分析的WSN贝叶斯信誉评价模型(/HTML)
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年01期
页码:
38
栏目:
出版日期:
2019-01-10

文章信息/Info

Title:
WSN Bayes Reputation Evaluation Model Based on Time Series Information Analysis
作者:
滕志军郭力文吕金玲侯颜权
文献标志码:
A
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可以更快收敛。
Abstract:
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 track 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 ithe 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 mailcious node could converge more quickly. 
更新日期/Last Update: 2019-03-02