Internet quality abnormal analysis with k-means clustering

The anomaly detection using data mining techniques has widely aroused interests in recent years. This paper proposes a novel algorithm to do the Internet quality abnormal analysis. The algorithm is developed with k-means clustering method to analyze the real-time data of Internet quality acquired at the testbed of Beijing University of Posts and Telecommunications (BUPT) for the first time. The main objective of this paper is to construct a statistical Internet quality abnormal analysis system capable of classifying the Internet quality data as normal or abnormal. For this purpose the algorithm constructs a model in training process. The data of Internet quality are compared with the model and data with distinct deviation from the model is marked as abnormal. The algorithm can split the abnormal data which are applied as a guide to further investigation of Internet anomaly root cause. And the result proved to be credible, which will be useful for the network administrators to manage the network.