We adopted a bibliometrical approach to evaluate the status of animal behavior research in China. Experimental results demonstrate that our approach can be applied to anomaly behavior detection and mainstream behavioral habits analysis. NetFlow data collected from some border routers of China Education Research Network backbone (CERNET) is used to verify the method. These classes are established by a characteristic spectrum matched IP address role mining algorithm designed in this paper. Nine single-attribute and thirty-nine dual-attribute metrics are extracted from four dimensions to compose the IP address traffic characteristic spectrum, which is used to profile the behavior of all IPs in the observed network and provide the data for the behavior description of each class of IP. The traffic behavior metrics are defined in four dimensions including the duration time, the peer address, the application types and the number of packets and bytes contained in the flow, which corresponds to temporal dimension, spatial dimension, category dimension and intensity dimension, respectively. Towards this end, a unidirectional IP flow record is used to represent an independent IP activity. These similar traffic behaviors can be depicted by a specific behavior pattern called IP address role in our work. Researches demonstrate that there are some similarities of the traffic behavior among different IPs, hence, they can be clustered based on the behavior similarity. The discovery and description of the IP traffic behavior is of great significance for both network operation management and network security monitoring.
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