Clustering Method of Cloud Platform Abnormal Transmission Data Based on Hilbert Similarity

doi:10.3969/j.issn.1006-2475.2023.09.004

Abstract

Abstract: The difference of abnormal transmission data of cloud platform is very small， which leads to poor accuracy of clustering of abnormal transmission data of cloud platform. Therefore， this paper proposes a clustering method of abnormal transmission data of cloud platform based on Hilbert similarity. The proposed method collects the abnormal transmission data of the cloud platform， maps the collected data into the Hilbert space， and constructs the Hilbert index to obtain the discrete probability distribution of indexs. Wavelet basis is used to analyze the sensitivity of abnormal transmission data of cloud platform， and the wavelet basis with relatively low sensitivity is selected for wavelet decomposition of abnormal transmission data. The proposed method calculates the similarity value in Hilbert space， divides it into a same data set， accurately divides the cloud platform abnormal transmission data， and realizes the cloud platform abnormal transmission data clustering. The experimental results show that the number of correct clustering data of the proposed method is 97 groups， and the time for clustering abnormal transmission data is only 146 s， which can effectively distinguish the abnormal transmission data in the cloud platform.

Key words: Hilbert similarity, cloud platform, abnormal data transmission, clustering, data division, low consumption time

CLC Number:

TP393.03

WANG Hong-jie, XU Sheng-chao. Clustering Method of Cloud Platform Abnormal Transmission Data Based on Hilbert Similarity[J]. Computer and Modernization, 2023, 0(09): 27-31.

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