网络监控中多元大数据分析的可解释特征学习
Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring
AI总结：
本文扩展了MBDA方法以实现自动特征推导，结合可解释模型与并行处理，用于大规模网络监控中的异常检测与诊断。
摘要：
人们对开发用于评估通信网络性能的新型数据驱动模型的兴趣日益浓厚。对于网络监控和故障排除等许多应用而言，如果数据模型无法被人类操作员解释，那么它的用处就很有限。在本文中，我们提出了 Multivariate Big Data Analysis (MBDA) 方法的扩展，这是一种最近提出的可解释数据分析工具。在此扩展中，我们提出了一种自动推导特征的解决方案，这是在数据量巨大时应用 MBDA 的关键步骤。由此产生的网络监控方法使我们能够检测和诊断不同的网络异常，其数据分析工作流将可解释和交互式模型的优势与并行处理的能力相结合。我们将扩展的 MBDA 应用于两个案例研究：UGR'16，一个用于异常检测的基准流式真实流量数据集；以及 Dartmouth'18，迄今为止已知的最长、最大的 Wi-Fi 轨迹。
英文摘要：
There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows us to detect and diagnose disparate network anomalies, with a data-analysis workflow that combines the advantages of interpretable and interactive models with the power of parallel processing. We apply the extended MBDA to two case studies: UGR'16, a benchmark flow-based real-traffic dataset for anomaly detection, and Dartmouth'18, the longest and largest Wi-Fi trace known to date.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
