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Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects. 

Abstract Smart Grid (SG) research and development has drawn much attention from academia, industry and government due to the great impact it will have on society, economics and the environment. Securing the SG is a considerably significant challenge due the increased dependency on communication networks to assist in physical process control, exposing them to various cyber‐threats. In addition to attacks that change measurement values using False Data Injection (FDI) techniques, attacks on the communication network may disrupt the power system's real‐time operation by intercepting messages, or by flooding the communication channels with unnecessary data. Addressing these attacks requires a cross‐layer approach. In this paper a cross‐layered strategy is presented, called Cross‐Layer Ensemble CorrDet with Adaptive Statistics(CECD‐AS), which integrates the detection of faulty SG measurement data as well as inconsistent network inter‐arrival times and transmission delays for more reliable and accurate anomaly detection and attack interpretation. Numerical results show that CECD‐AS can detect multiple False Data Injections, Denial of Service (DoS) and Man In The Middle (MITM) attacks with a high F1‐score compared to current approaches that only use SG measurement data for detection such as the traditional physics‐based State Estimation, ECD‐AS strategy and other machine learning classification‐based detection schemes.