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• Unbalanced parameter correction model against false data injection attacks. • Cyber-physical security framework for bad data analysis of FDI attacks. • Real-time multiple simultaneous measurement and parameter false data injection processing. This paper presents a correction model for malicious, unbalanced parameter false data injection cyber-attacks. Current state-of-art solutions can detect, identify and correct balanced parameter false data injection cyber-attacks. Thus, they consider all the parameters as equal in error, which means the methods will only work when the same percentage attack happens to each parameter. In this paper, a new correction model using a parameter correction Jacobian matrix, τ , and a Taylor series approximation is presented. A framework for measurement gross error analysis is deployed in processing and analyzing cyber-attacks. Chi-square χ 2 Hypothesis Testing applied to the normalized composed measurement error (CMEN) is considered for cyber-attacks detection, while the largest CMEN error test is used for identification. Validation is performed on the IEEE 14-bus and 118-bus systems. Easy-to-implement model, without hard-to-design parameters, built on the classical weighted least squares solution, highlights potential aspects for real-life implementation. [ABSTRACT FROM AUTHOR]