This chapter is concerned with the security sliding mode control (SMC) problem for Markov jump systems (MJSs) suffering from both physical and communication constraints, that is, the actuator saturation and injection attacks. Different from the injection attacks considered in Chap. 6 whose nonlinear upper bound is assumed to be known, in this chapter, we relax this assumption. With the help of the learning capability of neural networks, the nonlinear upper bound of injection attacks is estimated, and the security control strategy based on neural networks is proposed to improve the reliability of control. Considering the transient performance implementation, a time-parameter-dependent sliding mode controller is proposed to guarantee the reachability of the specified sliding surface within a given finite time, in which the adaptive rules of neural network parameters are designed to reduce the effects of the considered attack. The reaching and sliding motion phases are, respectively, analyzed and sufficient conditions are achieved to guarantee the stochastic finite-time boundedness (SFTB) of the closed-loop system. Eventually, a simulation example is given, combined with a genetic algorithm, to illustrate the validity of the proposed adaptive security control method.