Jamming games between a cognitive radio enabled secondary user (SU) and a cognitive radio enabled jammer are considered, in which end-user decision making is modeled using prospect theory (PT). More specifically, the interactions between a user and a smart jammer regarding their respective choices of transmit power are formulated as a game under the assumption that end-user decision making under uncertainty does not follow the traditional objective assumptions stipulated by expected utility theory, but rather follows the subjective deviations specified by PT. Two PT-based static jamming games are formulated to describe how subjective SU and jammer choose their transmit power to maximize their individual signal-to-interference-plusnoise ratio (SINR)-based utilities under uncertainties regarding the opponent's actions and channel states, respectively. The Nash equilibria of the games are presented under various channel models and transmission costs. Moreover, a PT-based dynamic jamming game is presented to investigate the long-term interactions between a subjective and smart jammer according to a Markov decision process with uncertainty on the SU's future actions and channel variations. Simulation results show that the subjective view of an SU tends to exaggerate the jamming probabilities and decreases its transmission probability, thus reducing the average SINR. On the other hand, the subjectivity of a jammer tends to reduce its jamming probability and thus increases the SU throughput.
Accurate malware detections on mobile devices such as smartphones require fast processing of a large number of data and thus cloud offloading can be used to improve the security performance of mobile devices with limited resources. The performance of malware detection with cloud offloading depends on the computation speed of the cloud, the population sharing the cloud resources and the bandwidth of the radio access. In the paper, we investigate the offloading rates of smartphones connecting to the same security server in a cloud under dynamic network bandwidths and formulate their interactions as a non-cooperative mobile cloud offloading game. The Nash equilibrium of the mobile cloud offloading game and the existence condition are presented. An offloading algorithm based on Q-learning is proposed for smartphones to determine their offloading rates for malware detection with unknown parameters such as transmission costs. Simulation results show that the proposed offloading strategy can achieve the optimal rate and improve the user's utility under dynamic network bandwidths.
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