Orthogonal projection for anomaly detection in networking datasets
In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efcient resource planning in advance involves a lot of laborintensive eforts. Consequently, network usage analysis is a very useful tool that allows network administrators to fnd patterns and anomalies. Whilst pattern detection provides administrators the ability to defne the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering diferent locations in the Milan metropolitan area, diferentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques.
Acknowledgements This publication has been possible thanks to the funding granted by the Ministry of Economy, Science and Digital Agenda of the Junta de Extremadura and by the European Regional Development Fund of the European Union under the projects IB18003, IB20040, GR21097 and GR21040, and it is also supported by the Spanish Ministry of Science and Innovation (Ref. PID2019-110315RB-I00 APRISA). Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
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