Bayesian mechanisms and learning for wireless networks security with QoS requirements

When there are strategic and malicious users in a wireless network, the resource allocation is complicated due to the information limitation about the nature of users and network parameters. Bayesian games are appropriate tools to analyze the network resource allocation with heterogeneous users. We consider a scenario with arbitrary number of malicious users in the network, in which individual users gather probabilistic information about the density of malicious users. Users and the base station observe the network over a long time period and modify their actions accordingly. The power allocation in wireless networks which we consider in this paper, is subject to Quality of Service (QoS) requirements. We consider Bayesian pricing mechanisms where the prices are modified using the Bayesian information about types of the users to satisfy the QoS requirements. We also give detection methods based on regression learning algorithms which are used for forming the probability of a user being malicious. The utilities of the users are formed by observing the power strategies of the users and the anomalies are detected. We obtain numerically, the Bayesian Nash Equilibrium (BNE) points of the Bayesian games. We also evaluate the effect of incomplete information on the satisfaction of the QoS requirements of the users in the mechanisms. These mechanisms are with prices which were originally developed for networks with complete information.