## 1 Introduction  
The Power System State Estimator (PSSE) is a major tool for real-time grid monitoring. The end-goal of PSSE is to estimate the system states, typically buses complex voltages, given a set of measurements. Several protection schemes and grid functionality rely on the output of the State Estimation (SE) process. The main inputs of PSSE are measurements set and model parameters. The first is a collection of different system measurement types. For example, circuit breaker status, real and reactive power flows, real and reactive power injection, and voltage magnitudes. The measurement model parameters represent the components of the underlying physical system. With any perturbation in the measurement set and/or model parameters, the PSSE will result in a wrong estimate of the system states. Much research has addressed measurement cyber-attacks. These are usually modeled as False Data Injection (FDI). Measurement model parameters cyber-attacks, on the other hand, have limited research in the field of power systems. In fact, these parameters are considered static and without error during the SE process. Hence, no monitoring scheme is presented in real-life applications. These parameters are prone to cyber-attacks. The cyber-attack in this context could be in the form of an external entity who is able to access the database and alter some of those parameters, or an internal entity who is able to gain super user privileges to change the database [1; 2; 3; 4; 5]. The former is a class of cyber-attack called Remote to User attack (R2U) while the latter is known as User to Root attack (U2R). In the literature, research on detecting FDI pertaining to SE measurements is much explored [3, 6, 7, 8, 9]. The work in [10, 11, 12, 13] investigated FDI attack in measurements only. Moreover, a DC model state estimation is considered. The work in [14] considered attack into states in addition to measurement FDI. However, the DC model assumes that states are linearly related to measurements. In addition, voltage magnitudes are assumed to be 1 pu. Such assumption is not accurate in some studies where accurate system model is needed. The AC state estimation, on the other hand, provides an accurate model compared to DC state estimation, since the relationship between states and measurements is non-linear. The work in [15] proposed a convexification framework for the AC state estimation based on semi-definite programming (SDP) for solving cyber attack pertaining measurements sensors. In addition to modeling solutions, Machine Learning (ML) based solutions are also presented [16, 17, 18]. The problem of detecting cyber-attacks in the measurement model parameters, on the other hand, has been much less considered [19]. Further, the presented solutions considered that cyber-attacks on measurements have been already corrected [20]. However, if a simultaneous attack happened, i.e., on measurements and parameters, how can a measurement correction be made? Existing work towards parameter cyber-attacks [21, 22, 23, 24] considers a two-step approach: detection and correction. In the detection step, the measurements' residual is analyzed and a pattern is extracted. An attack to a line parameter would result in the normalized residual of the measurements associated with that line to have a higher value compared to the other measurements, assuming no FDI attack [25]. In the correction step, the line's parameters are corrected in an iterative process using WLS in conjunction with Taylor series expansion. After correction, a SE routine is executed again to check if the normalized residual test does not detect errors. Otherwise, the correction routine is repeated until SE does not flag. In [26], errors on system parameters are addressed while estimating system states. Hence, an augmented objective function is built on the minimization of measurement and parameter residuals.