Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring
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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 language | English |
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Journal | IEEE Transactions on Network and Service Management |
Volume | 21 |
Issue number | 3 |
Pages (from-to) | 2926 - 2943 |
ISSN | 1932-4537 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Publisher Copyright:
Authors
- Analytical models, Anomaly Detection, Big Data, Dartmouth Campus Wi-Fi, Data models, Data visualization, Interpretable Machine Learning, Monitoring, Multivariate Big Data Analysis, Network Monitoring, Principal component analysis, Representation learning, UGR’16
Research areas
ID: 389672967