Secondary User Data Capturing for Cognitive Radio Network Forensics under Capturing Uncertainty

Secondary user data capturing is a fundamental building block for cognitive radio network forensics. It faces great challenges mainly due to the unknown secondary user behavior, wide spectrum, and packet capturing uncertainty. There is a lack of fundamental understanding of the data capturing problem in theory. In this paper, for the first time, we formulate the dynamic sniffer channel assignment problem without the knowledge of users´ behavior patterns as a non-stochastic multiarmed bandit (MAB) problem. Moreover, we consider a more practical scenario with the consideration of packet capturing uncertainty and switching cost. We then propose an efficient solution to solve the problem and analyze the regret of our policy. Finally, a simulation study validates the convergence of our method.