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Network forensics is an emerging interdiscipline used to track down cyber crimes and detect network anomalies for a multitude of applications. Efficient capture of data is the basis of network forensics. Compared to traditional networks, data capture faces significant challenges in cognitive radio networks. In traditional wireless networks, usually one monitor is assigned to one channel for traffic capture. This approach will incur very high cost in cognitive radio networks because it typically has a large number of channels. Furthermore, due to the uncertainty of the primary user's behavior, cognitive radio devices change their operating channels dynamically, which makes data capturing more difficult. In this paper, we propose a systematic method to capture data in cognitive radio networks with a small number of monitors. We utilize incremental support vector regression to predict packet arrival time and intelligently switch …