无线传感网中基于DPAM-MD算法的恶意节点识别研究

随着无线传感器网络的不断发展，恶意节点对其安全造成了极大的威胁。传统的基于信誉阈值的模型无法准确的识别亚攻击性等恶意节点，而且会出现低识别率和高误判率等问题。为了解决这些问题，引入了基于DPAM-MD算法的新型恶意节点识别方法，在传统信誉阈值判断模型的基础上，通过结合曼哈顿度量和DPAM算法识别出亚攻击性节点。算法中提出一种新型的基于密度的聚类算法，并结合簇间和簇内距离均衡化的目标函数，将所有的节点进行分类。该算法可以提高聚类质量，有效缩短聚类时间，提高了恶意节点识别的效率。经仿真实验结果验证，改进后的算法对识别特征不明显的恶意节点效果十分显著。

Research of malicious nodes identification based on DPAM-DM algorithm for WSN

With the continuous development of wireless sensor networks, malicious nodes was a major threat to its security. The traditional credit threshold model could not accurately identify the malicious nodes, such as sub attack node. And there will be a low recognition rate and high false positive rate and other issues. To solve these problems, a new recognition method based on DPAM-MD algorithm was introduced, which was based on the traditional credit threshold model, combining manhattan metric with DPAM algorithm to distinguish the sub attack node. A new clustering algorithm based on density was proposed, which was based on the objective function of the distance equalization between the intra-cluster and the inter-cluster. The proposed algorithm can improve the quality of clustering, shorten the time of clustering, and improve the efficiency of recognition of malicious nodes .Through simulation results ,verify the improved algorithm effectively identify undistinguishable node.