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 language | English |
---|---|
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
Keywords
- 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