In this paper, we investigate how to achieve the unpredictability against malicious inferences for linear systems. The key idea is to add stochastic control inputs, named as unpredictable control, to make the outputs irregular. The future outputs thus become unpredictable and the performance of inferences is degraded. The major challenges lie in: i) how to formulate optimization problems to obtain an optimal distribution of stochastic input, under unknown prediction accuracy of the adversary; and ii) how to achieve the trade-off between the unpredictability and control performance. We first utilize both variance and confidence probability of prediction error to quantify unpredictability, then formulate two two-stage stochastic optimization problems, respectively. Under variance metric, the analytic optimal distribution of control input is provided. With probability metric, it is a non-convex optimization problem, thus we present a novel numerical method and convert the problem into a solvable linear optimization problem. Last, we quantify the control performance under unpredictable control, and accordingly design the unpredictable LQR and cooperative control. Simulations demonstrate the unpredictability of our control algorithm. The obtained optimal distribution outperforms Gaussian and Laplace distributions commonly used in differential privacy under proposed metrics.