Abstract
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.
Highlights
To maintain a high quality of service with regard to reliability, robustness and speed is of utmost importance for today’s wireless telecommunication networks
In the work presented here, we suggest the application of topic modeling (Blei et al 2003) as a means for anomaly detection within network behavior
We identified what significant information can be extracted from the topic model that would aid an operator while monitoring network performance
Summary
To maintain a high quality of service with regard to reliability, robustness and speed is of utmost importance for today’s wireless telecommunication networks. This importance is anticipated to dramatically increase in the future, where emergency services like ambulance, health service and fire department will rely almost entirely on wireless communication. A common approach towards anomaly detection is to define a region of normal behavior and label any observation in the data that does not belong to this region as an anomaly, (Chandola et al 2009). What constitutes as normal behavior can be largely sensitive to context and time, as well as the fact that what is to be considered “normal” can be constantly evolving
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