Cyber-physical security is essential for protecting critical infrastructure, such as water and electricity distribution networks. This study addresses false code injection attacks on cyber-physical systems (CPS) by proposing an adaptive optimal control strategy. The method ensures that system outputs remain within safe operational limits during attacks, preventing potential damage. An adaptive control system is developed to estimate and compensate for disturbed control signals in real-time using system data, thereby enhancing stability and resilience against cyber threats. The effectiveness of the approach is validated through graph theory, demonstrating its ability to improve CPS security and reliability. The research also identifies limitations in active cyber defense systems, particularly against zero-day attacks, and offers a practical solution using an adaptive optimal controller. Simulations highlight that the implemented FIR algorithm effectively filters noise and tracks reference signals, while the optimal controller autonomously derives control laws without requiring system dynamics equations. This work contributes to advancing adaptive control strategies for enhancing cyber-physical security in critical infrastructure.