Micro aerial vehicles (MAVs), in particular, quadcopters, have seen increasing adoption in many application domains due to their flexible operations and versatile functions. By taking advantages of the inherent system inertia properties of the physical platform during MAV operations, satisfactory flight performance of the vehicle can be achieved despite a variety of internal and external disturbances including environment perturbations and cyber-physical attacks, provided the fault detection and control strategy is maintained with a proper lower bound of its operating frequency. From the control design perspective, such frequency bound depends heavily on the sensitivity of the system response to its dynamic properties including inertia and dynamic couplings. For a cyber-physical system, which is subject to various cyber and physical attacks, there is usually a large sensitivity difference between physical time scale and cyber time scale. With this fact, a properly designed fault-tolerant and control strategy can leverage the inertia property of the system to arrive at an optimal control frequency to save computational power while maintaining the flight performance. For many current MAVs, their control programs are usually executed at a fixed high-frequency, which provides no significant performance gains at most of the time. In this work, we perform a sensitivity analysis and quantification of the vehicle system to guide the design and optimization of customized on-board control and computational resources. By quantifying system sensitivity and its variation across different control channels (DOFs), the computational resources can be re-distributed and optimized to detect and reject cyber-physical attacks more efficiently. With the proposed method, the cyber and physical resources of the vehicle can be allocated by system demands, which can further enhance vehicle reliability and accomplish complicated security tasks. To demonstrate the effectiveness of the proposed method, we have conducted the simulated flight test with the real vehicle parameters.