Topic modeling for anomaly detection in telecommunication networks
To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the result...
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Published in | Journal of ambient intelligence and humanized computing Vol. 14; no. 11; pp. 15085 - 15096 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain. |
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ISSN: | 1868-5137 1868-5145 1868-5145 |
DOI: | 10.1007/s12652-019-01372-5 |