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清华大学学报(自然科学版)  2017, Vol. 57 Issue (7): 687-694    DOI: 10.16511/j.cnki.qhdxxb.2017.25.023
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
无线Mesh网络恶意节点检测模型
杨宏宇, 李航
中国民航大学 计算机科学与技术学院, 天津 300300
Malicious node detection model for wireless Mesh networks
YANG Hongyu, LI Hang
School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
全文: PDF(1363 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 针对现有恶意节点检测方法对无线Mesh网络恶意节点检测效率低的问题,该文提出一种基于移动Ad-hoc网络更优方案(better approach to mobile Ad-hoc networking,BATMAN)路由协议的恶意节点检测模型(malicious node detection model based on BATMAN,MNDMB)。在无线Mesh网络中使用BATMAN路由协议,在网络节点上安装源节点消息解析模块,根据解析模块生成的参数和相应阈值的比较判断出可疑节点,通过一致性投票机制计算出可疑节点置信值作为恶意节点判定的标准。仿真验证结果表明:与现有方法相比,MNDMB在无线Mesh网络中具有较高的恶意节点检测率和较低的误报率。
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杨宏宇
李航
关键词 无线Mesh网络网络拓扑恶意行为投票置信值    
Abstract:Existing malicious node detection methods for wireless Mesh networks are not very efficient. This paper presents a malicious node detection method based on the mobile Ad-hoc networking (BATMAN) route protocol (MNDMB). The BATMAN route protocol is loaded into a wireless Mesh network in a source node message analysis module to generate the defection parameters and identify suspicious nodes depending on these parameters by comparisons to thresholds. Then, a multi-node voting mechanism is used to calculate the confidence value which is used as the criterion for judging the malicious node. Verification tests show that this protocol has higher detection rates and lower false positive rates in wireless Mesh networks than existing methods.
Key wordswireless Mesh network    network topology    malicious behavior    vote    confidence value
收稿日期: 2016-12-07      出版日期: 2017-07-15
ZTFLH:  TP393  
引用本文:   
杨宏宇, 李航. 无线Mesh网络恶意节点检测模型[J]. 清华大学学报(自然科学版), 2017, 57(7): 687-694.
YANG Hongyu, LI Hang. Malicious node detection model for wireless Mesh networks. Journal of Tsinghua University(Science and Technology), 2017, 57(7): 687-694.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.023  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I7/687
  图1 MNDMB模型结构
  图2 OGM 结构图
  图3 OGM 解析模块处理流程
  图4 恶意节点判定模块处理流程
  图5 改进的K/N 投票算法
  表1 其他仿真参数设置
  图6 NS2平台网络布局
  图7 不同采样周期的恶意节点检测效果
  图8 3种方法的恶意节点检测效果
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