## 1 Introduction
### Related Works  
The benefits and risks involved in utilizing communications technologies alongside legacy electrical power systems have been widely reviewed [9; 10; 11; 12; 13]. Intrusion into a communications network by a pernicious user who is aiming to target the integrity of the data can have a catastrophic impact on the secure and reliable operation of an SG [14; 15; 16]. Therefore, in the context of the security of SGs, understanding the nature of the assault and identification of compromised data has been the focus of research in electric power systems. The conventional state estimator in a PCC utilizes BDD to single out and disassociate the bad data for state estimation. However, [PERSON] et al. [17] demonstrated that a smart attacker who has information on the network topology can realize the construction of a set of falsified data that can dodge legacy BDD. This type of attack is known as an unobservable (or covert) cyber assault. Many schemes considering the construction of intrusion assaults against state estimation, and the subsequent defense measures against them, have been discussed in the literature [18; 19; 14; 13; 15; 16; 17; 18; 19; 20; 21]. [PERSON] et al. [18] proposed a decentralized conjunctive rule-based majority voting algorithm to detect compromised or assaulted phase measurement units. [PERSON] et al. [19] proposed cumulative sum  
Figure 1: Covert cyber deception assault in a smart grid communications network. PCC, power control center; OPF, optimal power flow; AGC, auto-generation control; RTU, remote terminal unit.  
hypothesis test-based bad-data detection in a state estimator. [PERSON] et al. [22] demonstrated that a data integrity assault can methodically result in a considerable economic loss in real-time market operations. Similarly, [PERSON] and colleagues [23] studied the fiscal impact of a false data injection attack on electric power market operations. An encryption-based security mechanism integrated into power system devices was proposed [24] to improve the security of the power system against FDI attacks. 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. Thousands of sensors or RTUs are employed in power grids, spanning over a vast geographical area. Practically, defense mechanism can be designed to protect a limited set of critical RTUs and corresponding measurement features (MFs). Therefore in the context of SG cyber-security and computational complexity, the selection of distinctive features becomes a promising strategy to detect the CCD assault in real time [5]. [PERSON] et al. [6] proposed a joint-transformation-based scheme to detect CCD assault. They utilized 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 FE technique to tackle the dimensionality issue in the SE-MF dataset and then employed a statistical method-based AD mechanism to detect the CCD assault.  
In summary, existing works on CCD assault detection in SGs only have generally considered feature extraction or transformation [5; 6; 7] in the context of cybersecurity and the curse of dimensionality. To the best of our knowledge, the selection of distinguishing features from the SE-MF dataset in the context of SG security is still an open problem.