摘要： 根据不同干扰攻击所导致的网络现象之间的差异,提出一种基于现象-攻击关联图的干扰检测算法。该算法分为学习和检测2个阶段。学习阶段各节点通过学习无干扰和有干扰场景下的样本,基于各类测度的变化划分网络现象,建立现象-攻击关联图。检测阶段各节点依据各自的关联图,根据观察到的现象判断自身是否受到干扰攻击以及攻击类型,采用匹配度对检测结果的准确性进行评估。在NS3上的仿真结果表明,该算法可以准确检测按需、随机和持续干扰攻击。

关键词: 无线自组织网络, 干扰检测, 关联图, 贡献度, 匹配度

Abstract: According to the difference between the network phenomena caused by different jamming attacks,a jamming detection algorithm based on symptom-attack association graph is proposed,which consists of two phases,i.e.learning and detection phases.At the learning phase,by learning from the various samples collected from both jamming and jamming-free scenarios,the nodes divide the network phenomenon based on the changes of various measures and establish a symptom-attack association graph.Then,at the detection phase,each node judges whether it is subjected to jamming attack and attack type on the basis of their own association graph according to their own observations,and evaluates the accuracy of the detection result by matching degree.The simulation results on NS3 validate that the proposed algorithm can efficiently detect and classify the typical jamming attacks,such as reactive,random and constant jamming attacks.

Key words: wireless ad hoc network, jamming detection, association graph, contribution degree, matching degree

中图分类号: 

• TP391