An adaptive trust-Stackelberg game model for security and energy efficiency in dynamic cognitive radio networks

Due to the potential of cooperative cognitive radio networks (CCRNs) in addressing the spectrum scarcity problem in wireless communication networks, CCRN has become a subject of active research. For example, security and energy efficiency are two salient areas of research in CCRNs. In this paper, we propose a novel adaptive trust-Stackelberg game model designed to (a) improve the energy efficiency and (b) defend against insider attacks in CCRNs. More specifically, the distributed learning algorithm (DLA) for the relays in our model, inspired by the stochastic learning automata, allows the system to achieve Stackelberg equilibrium in the proposed game; and the trust evolution based on evolutionary stable strategy algorithm (TEEA) allows the primary user to defend against insider attacks efficiently and adaptively adjust the trust evolution in dynamic CCRNs. We demonstrate the utility of the proposed model comparing with other models using a numerical investigation. The numerical results show that the proposed model can improve the performance in energy efficiency and defending against insider attacks with an appropriate cooperation between primary users and relays.