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Research on Network Configuration Verification Based on Association Analysis

Published: 13 December 2022 Publication History

Abstract

This paper studies the application of association analysis in the scenario of massive network configuration verification, and puts forward a kind of network configuration anomaly detection method and system based on association analysis. We creatively use the weak association rules in association analysis to detect configuration anomaly. And we can generate a configuration anomaly verification model through training the processed configuration data, which is applied to scan the massive configuration data and output configuration anomaly results. At the same time, we constructed a network configuration verification system based on ZTE's AI platform and verified the effectiveness of the algorithm and model by using the massive realistic configuration data collected from the existing networks. The experimental results show that the precision and recall of the proposed network configuration verification system are above 80%.

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    CSAE '22: Proceedings of the 6th International Conference on Computer Science and Application Engineering
    October 2022
    411 pages
    ISBN:9781450396004
    DOI:10.1145/3565387
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 13 December 2022

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    Author Tags

    1. Association analysis
    2. Configuration verification
    3. Model training
    4. Weak association

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    Overall Acceptance Rate 368 of 770 submissions, 48%

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