A dynamic network anomaly detection method based on trend analysis
Tong Sheng Sun, Yan Liu, Jing Chen · 2017
Detecting abnormal behavior during network evolution is an important and challenging data analysis task at present, it is named as dynamic network anomaly detection in this paper. The network abnormal behavior differs from that of normal behavior, the probability of occurrence stays relatively low but may cause serious damages once happened. This paper takes the topological structure of the network as the research object and identifies the network anomaly through the change of network structure. Based on Cox-stuart test, we put forward a new type of method to detect dynamic anomaly. This method refers to an alarm of monitoring the trend change of the network structure parameters. Finally, an experiment was carried out in the real dynamic network-Enron Email Network, which verified that the dynamic network anomaly detection method proposed in this paper can effectively detect the network anomaly behavior.