一种基于时序信誉的WSNs恶意节点检测算法

A malicious node detecting algorithm for WSNs based on time sequence credibility

大多数无线传感器网络(WSNs)部署在户外环境中,恶意节点对WSNs安全存在巨大威胁。信誉阈值模型在识别恶意节点这一领域有着不俗的表现,但随着攻击技术的升级,恶意节点变得更加隐秘而难以被发觉。经分析引入时序和相似度的概念对原有信誉阈值模型加以改进,由欧几里得度量和K-mediods算法可以有效地分辨这一类恶意节点。经仿真实验验证,对识别特征不明显的恶意节点改进后的算法效果十分显著。

Most wireless sensor networks（ WSNs） are deployed in outdoor environment,malicious node is a major threat to the safety of WSNs. Credit threshold model has a good performance in the field to identify the malicious node,but with the attack technology upgrade,the malicious node become more conceal and difficult to be found.The credit threshold model is improved by introducing the concept of time sequence and similarity through analysis,using Euclidean metric and K-mediods algorithm can distinguish this kind of malicious node effectively.Through simulation test,verify the improved algorithm effectively identify undistinguishable malicious node.