Author ORCID Identifier
David Kotz: https://orcid.org/0000-0001-7411-2783
Document Type
Other
Publication Date
4-18-2023
Publication Title
arXiv
Department
Department of Computer Science
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.
Original Citation
José Camacho, Rasmus Bro, and David Kotz. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. Technical Report number 1907.02677, arXiv, April 2023.
Dartmouth Digital Commons Citation
Camacho, José; Bro, Rasmus; and Kotz, David, "Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring" (2023). Dartmouth Scholarship. 4312.
https://digitalcommons.dartmouth.edu/facoa/4312
