Author ORCID Identifier
https://orcid.org/0000-0001-7411-2783
Document Type
Article
Publication Date
6-2024
Publication Title
IEEE Transactions on Network and Service Management
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.
DOI
10.1109/TNSM.2024.3368501
Original Citation
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, volume 21, number 3, pages 2926–2943. IEEE, June 2024. doi:10.1109/TNSM.2024.3368501.
Dartmouth Digital Commons Citation
Camacho, José; Wasielewska, Katarzyna; Bro, Rasmus; and Kotz, David, "Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring" (2024). Dartmouth Scholarship. 4325.
https://digitalcommons.dartmouth.edu/facoa/4325
