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The Increasing Interconnection Of Cyber-physical Systems (CPS), Industrial Control Networks, And Internet Of Things (IoT) Infrastructures Has Exposed Them To Sophisticated Cyber-attacks Targeting Both Data Integrity And System Stability. Traditional Security Mechanisms Often Adopt Uniform Protection Strategies Without Considering The Varying Criticality Of Data, Leaving High-value Information Particularly Vulnerable To Targeted Attacks. To Address This Challenge, This Study Investigates A Data-importance-aware Attack Strategy, Where Adversaries Selectively Manipulate Or Disrupt Data Based On Its Role In Decision-making And Control Processes. By Modeling The Attack Impact With Respect To Data Sensitivity, We Reveal How Resource-constrained Adversaries Can Maximize System Disruption With Minimal Effort. Building On These Insights, We Propose A Secure Control Countermeasure That Dynamically Prioritizes Defense Resources According To Data Importance, Ensuring Robust System Performance Under Adversarial Conditions. The Framework Integrates Importance-weighted Anomaly Detection, Resilient State Estimation, And Adaptive Control Reconfiguration To Mitigate The Impact Of Strategic Attacks. Experimental Evaluations On Simulated CPS And IoT Scenarios Demonstrate That The Proposed Approach Not Only Reduces Vulnerability To Data-targeted Attacks But Also Optimizes Security Resource Allocation. This Work Provides A Novel Perspective On Both Offensive And Defensive Strategies In Cyber-physical Security, Contributing To The Design Of Resilient, Importance-aware Secure Control Mechanisms.