Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring

[camacho:networkmetrics-j]

José Camacho, Katarzyna Wasielewska, Rasmus Bro, and David Kotz. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. IEEE Transactions on Network and Service Management. IEEE, February 2024. doi:10.1109/TNSM.2024.3368501. ©Copyright IEEE (open access). Accepted for publication. Revision of camacho:networkmetrics-tr2.

Abstract:

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.

Citable with [BibTeX]

Projects: [wifi-measure]

Keywords: [wifi]

Available from the publisher: [DOI]

Available from the author: [bib]
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[Kotz research]