This paper considers a synthetic counter-measure, combining transmission scheduling and defensive deception, to defend against jamming attacks in remote state estimation. In the setup studied, an attacker sabotages packet transmissions from a sensor to a remote estimator by congesting the communication channel between them. In order to efficiently degrade the estimation accuracy, the intelligent attacker tailors its jamming strategy by reacting to the real-time information it collects. In response to the jamming attacks, the sensor with a long-term goal will select the transmission power level at each stage. In addition, by modifying the real-time information intentionally, the sensor creates asymmetric uncertainty to mislead the attacker and thus mitigate attacks. Considering the dynamic nature of the process, we model the strategic interaction between the sensor and the attacker by a general stochastic game with asymmetric information structure. To obtain stationary optimal strategies for each player, we convert this game into a belief-based dynamic game and analyze the existence of its optimal solution. For a tractable implementation, we present an algorithm that finds equilibrium strategies based on multi-agent reinforcement learning for symmetric-information stochastic games. Numerical examples illustrate properties of the proposed algorithm.