基于关联分析的网络配置验证研究

Xiaoming He, Shao-wen Li, Zijing He, Xing Peng
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引用次数: 0

摘要

研究了关联分析在海量网络配置验证场景中的应用,提出了一种基于关联分析的网络配置异常检测方法和系统。我们创造性地在关联分析中使用弱关联规则来检测配置异常。通过对处理后的配置数据进行训练,生成配置异常验证模型,应用该模型对海量配置数据进行扫描,输出配置异常结果。同时,我们基于中兴通讯的AI平台构建了网络配置验证系统,并利用从现有网络中收集的大量现实配置数据,验证了算法和模型的有效性。实验结果表明,所提出的网络配置验证系统的准确率和查全率均在80%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Network Configuration Verification Based on Association Analysis
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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