Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

• URL: http://arxiv.org/abs/1907.02677v3
• Date: Fri, 1 Mar 2024 10:28:03 GMT
• Title: Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring
• Authors: Jos\'e Camacho, Katarzyna Wasielewska, Rasmus Bro, David Kotz
• Abstract summary: We present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. 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. 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.
• Score: 0.4342241136871849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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