We consider game theoretic models of wireless medium access control (MAC) in which each transmitter makes individual decisions regarding their power level or transmission probability. This allows for scalable distributed operation; however, it can also enable users to pursue malicious objectives such as jamming other nodes to deny them service. We study games with two types of players: selfish and malicious transmitters. Each type is characterized by a utility function depending on throughput reward and energy cost. Furthermore, we focus on the setting where the transmitters have incomplete information regarding other transmitters' types, modeled as probabilistic beliefs. We first analyze a power-controlled MAC game in which the nodes select powers for continuous transmissions and then extend this to a random access MAC in which nodes choose transmission probabilities. For each case, the Bayesian Nash equilibrium strategies are derived for different degrees of uncertainty, and the resulting equilibrium throughput of selfish nodes is characterized. We identify conditions in which the throughput improves with increasing type uncertainty and introduce Bayesian learning mechanisms to update the type beliefs in repeated games. For unknown types and costs, we also specify the equilibrium cut-off thresholds for monotonic transmission decisions. The analysis provides insights into the optimal defense mechanisms against denial of service attacks at the MAC layer in wireless networks.