Recognition of abnormal flow of power grid data server based on FARIMA model
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Abstract
At present, the average delay of the traffic anomaly identification method of power grid data server is too long and the packet loss rate is too high. Therefore, a traffic anomaly identification method of power grid data server based on FARIMA model is proposed. The multifractal measure method is used to extract one dimension of power grid data flow characteristics, describe the flow characteristics with large variance, and calculate the power grid data server flow threshold by using the regression algorithm of FARIMA model, so as to analyze the data server flow behavior of power grid under normal operation conditions, and detect the flow threshold at this time, The FARIMA model and parameter estimation method are used to identify the abnormal flow of power grid data server. Experimental results show that the proposed method can effectively shorten the average delay and reduce the packet loss rate.
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Published In
December 2023
371 pages
ISBN:9798400709203
DOI:10.1145/3639631
Copyright © 2023 ACM.
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Published: 16 February 2024
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ACAI 2023
ACAI 2023: 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2023
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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