在云对等网络中在线异常点的实时搜索和准确检测关系云对等网络的稳定和安全,由于云对等网络的在现异常点检测受到大量对等合法数据的干扰,网络波动幅度不大,检测困难。提出一种基于密度部分存储优化的云对等网络在线异常点检测算法,通过计算局部节点数据的在线时间复杂度实现对路由交换数据序列的初始特征和先验信息的预估计,适当增大存储空间开销来换取时间效率,实现零跳搜索和对异常点的准确检测。研究结果表明,采用该算法进行云对等网络的异常点检测,检测准确率大幅提高,执行开销降低,保证了对云对等网络安全性,提高了动态监测能力。
In the cloud peer-to-peer network, the real time search and accurate detection of abnormal point is key for the stability and security of the network, it is affected by a lot of interfere with legitimate data, and the network fluctuation range is not big. The detection is difficult. An improved online abnormal point detection algorithm is proposed based on density storage optimization, the local node data online time complexity is calculated for estimating the initial feature and a priori information of routing switching data. The storage space is increased for getting the time efficiency. The zero hop search and accurate detection are realized. Simulation results show that the algorithm can improve the detection accuracy greatly, the execution time is reduced, and security of cloud peer-to-peer network is ensured, the dynamic monitoring abili-ty is improved.