CAJ | 학술논문

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。
재운대등망락중재선이상점적실시수색화준학검측관계운대등망락적은정화안전，유우운대등망락적재현이상점검측수도대량대등합법수거적간우，망락파동폭도불대，검측곤난。제출일충기우밀도부분존저우화적운대등망락재선이상점검측산법，통과계산국부절점수거적재선시간복잡도실현대로유교환수거서렬적초시특정화선험신식적예고계，괄당증대존저공간개소래환취시간효솔，실현령도수색화대이상점적준학검측。연구결과표명，채용해산법진행운대등망락적이상점검측，검측준학솔대폭제고，집행개소강저，보증료대운대등망락안전성，제고료동태감측능력。
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.