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Anomaly detection, which is a method of intrusion detection, detects anomaly behaviors and protects network security. Data mining technology has been integrated to improve the performance of anomaly detection and some algorithms have been improved for anomaly detection field. We think that most data mining algorithms are analyzed on static data sets and ignore the influence of dynamic data streams. Data stream is the potentially unbounded, ordered sequence of data objects which arrive over time. The entire data objects cannot be stored and they need to be handled in one-time scanning. The data distribution of data stream may change over time and this phenomenon is called concept drift. The properties of data stream make analysis method different from the method based on data set and the analysis model is required to be updated immediately when concept drift occurs. In this paper, we summarize the characteristics of data stream, compare the difference between data stream and data set, discuss the problems of data stream mining and propose some corresponding strategies. [ABSTRACT FROM AUTHOR]