基于图论的MANET入侵检测方法

Intrusion Detection Method for MANET Based on Graph Theory

移动Ad hoc网络(MANET)易遭受各种安全威胁,入侵检测是其安全运行的有效保障,已有方法主要关注特征选择以及特征权重,而忽略特征间潜在关联性,针对此问题该文提出基于图论的MANET入侵检测方法。首先通过对典型攻击行为分析,合理选择9种特征作为节点,依据欧式距离确定节点间的边以构建结构图。其次发掘节点(即特征)间关联性,综合考虑节点邻居规模属性和节点邻居之间的紧密程度属性,利用图论所对应的统计特性度分布和聚集系数具体实现两属性。最后对比实验结果证明此方法与传统方法相比平均检测率和误检率分别提高10.15%、降低1.8%。

Mobile Ad hoc NETwork（MANET） is vulnerable to various security threats,and intrusion detection is an effective guarantee for its safe operation.However,existing methods mainly focus on feature selection and feature weighting,and ignore the potential association among features.To solve this problem,an intrusion detection method for MANET based on graph theory is proposed.First of all,nine features are selected as nodes based on the analysis of typical attack behavior,and the edges among nodes are determined according to Euclidean distance so as to build the structure diagram.Secondly,the scale attributes of neighborhood nodes and the degree of closeness attributes among nodes are considered to explore（i.e.feature） the correlation among nodes,then the statistical properties degree distribution and clustering coefficient of graph theory are used to realize the above two attributes.Finally,contrasting experimental results show that compared with the traditional methods,the average detection rate and false detection rate of new method are improved by 10.15% and reduced by 1.8% respectively.