Detecting Contextual Network Anomaly in the Radio Network Controller from Bayesian Data Analysis

Abstract: This thesis presents Bayesian approach for a contextual network anomaly detection. Network anomaly detection is important in a computer system performance monitoring perspective. Detecting a contextual anomaly is much harder since we need to take the context into account in order to explain whether it is normal or abnormal. The main idea of this thesis is to find contextual attributes from a set of indicators, then to estimate the resource loads through the Bayesian model. The proposed algorithm offers three advantages. Firstly, the model can estimate resource loads with automatically selected indicators and its credible intervals. Secondly, both point and collective contextual anomalies can be captured by the posterior predictive distribution. Lastly, the structural interpretation of the model gives us a way to find similar nodes. This thesis employs real data from Radio Network Controller (RNC) to validate the effectiveness in detecting contextual anomalies.