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There is an increasing interest in the development of new data-driven modelsuseful to assess the performance of communication networks. For manyapplications, like network monitoring and troubleshooting, a data model is oflittle use if it cannot be interpreted by a human operator. In this paper, wepresent an extension of the Multivariate Big Data Analysis (MBDA) methodology,a recently proposed interpretable data analysis tool. In this extension, wepropose a solution to the automatic derivation of features, a cornerstone stepfor the application of MBDA when the amount of data is massive. The resultingnetwork monitoring approach allows us to detect and diagnose disparate networkanomalies, with a data-analysis workflow that combines the advantages ofinterpretable and interactive models with the power of parallel processing. Weapply the extended MBDA to two case studies: UGR'16, a benchmark flow-basedreal-traffic dataset for anomaly detection, and Dartmouth'18, the longest andlargest Wi-Fi trace known to date.

Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring