## 1 Introduction  
The emerging smart grid (SG) concept as a cyber-physical complex organization is being implemented through a composition of communications networks overlaying traditional power systems. Due to the electrical energy flow's vital dependency on communications technologies in the SG, its vulnerability to new malicious types of cyber attack is very high. Nation states are apprehensive about power grid privacy and security. Therefore, vigorous and secure communications management is essential to all aspects of the SG. The security, privacy and integrity of data and the information network have become a prime focus of research activities in SGs. Traditionally, bulk storage of the generated electricity is not possible, and hence, its generation should be closely equated to consumption; otherwise, there can be a deviation in the electrical quantities. Thus, the power control center (PCC) needs to monitor the power network closely to make sure that the operation of the power system is safe and reliable. State estimation (SE) is a fundamental approach employed in an energy management system (EMS) to monitor states in power networks. Fundamental elements (generation, transmission and consumption) of a power system, along with communications links, are illustrated in Figure 1. Distributed sensors, actuators and meters designated as remote terminal units (RTUs) are employed in electric power grids to aggregate measurements, including bus power insertions and branch power flows. These measurements are combined at the PCC via communications links and are further used to estimate the states (i.e., bus voltage angles). These state variables form the basis of suitable decisions by the EMS about auto-generation control (AGC) and optimal power flow (OPF) to keep electric power systems in a safe operating zone. On the one hand, the existence of a communications infrastructure is compulsory for the realization of efficient monitoring and intelligent control in the framework of an SG, but the communications infrastructure is prone to malicious cyber-assault threats [1; 2; 3], due to certain incentives for the attacker. Unidirectional flow of information in legacy power networks (i.e., from RTUs to PCC) makes it more important to study a particular type of malicious user behavior that attempts to target the integrity of the measurement data by inserting a deceptive bias value into the SE. Such malicious activity goes mostly undetected by bad-data detection (BDD) systems in the legacy PCC. We term this kind of attack a covert cyber deception (CCD) assault, but it is also known as a false data injection (FDI) attack, a cyber stealthy deception (CSD) attack, and so on [4]. Identification and removal of the susceptibilities or anomalies injected by a CCD assault are critically important because of their negative impacts on the safety and reliability of SGs. Methods reported in the literature to mitigate the effects of CCD assaults on SGs can be broadly divided into two classes: (1) protection-based defense; and (2) detection-based defense. [PERSON] et al. [5] utilized a variety of machine learning-based schemes to detect the CCD assault in SG. [PERSON] et al. [6] proposed joint-transformation-based scheme to detect CCD assault. They utilize the Kullback-Leibler distance to find out the difference between probability distributions obtained from measurement variations. However, they did not employ feature selection (FS)-based techniques in their work to tackle the dimensionality issue with increasing power system sizes. [PERSON] et al. [7] used the PCA-based feature extraction (FE) technique to tackle the dimensionality issue in the state estimation-measurement feature (SE-MF) dataset and then employed a statistical method-based anomaly detection (AD) mechanism to detect the CCD assault. Unlike the existing schemes, in this paper, we focus on the selection of the discriminating features from the SE-MF dataset utilizing genetic algorithm (GA) to tackle the curse of dimensionality [8].