###### Abstract
A proposal for using a fault-solving library coupled with a machine-learning algorithm to create self-healing functionality in computer systems was put forth by [11]. This paper briefly discusses the proposal, particularly the twin model approach. Self-healing functionality is vital for providing excellent quality of service (QoS) in cloud computing. With QoS increasingly critical to services offered by vendors of cloud computing, such services as software as service (SaaS), platform as service (PaaS), and infrastructure as service (IaaS), self-healing functions allow the network environment the ability to recover from failure situations that may occur within a software, network, or hardware part of the system, in such cases described in [12]. A technique was developed called self-configuring and self-healing of cloud-based resources RADAR. The principal issue that affects the optimal performance of the smart grid network is multifaceted failures in multiple areas of the network, such as network overload, systems intrusion, systems misconfiguration, etc. These failures can potentially cause severe setbacks to the economy and the quality of human life, which can be mitigated by applying self-healing functionality to the system, as demonstrated in recent research studies. Part of the myriad of solutions that have been proposed is using a fault-solving strategy library on a twin model system and a machine-learning (ML) algorithm to implement a self-healing mechanism in a smart grid. The ML algorithm compiles with the dataset derived from the fault-solving library and is then deployed to detect the anomalies within the cyber-physical system. The anomaly detection  
Figure 1: Chronological progression of industrial revolutions: from the 1 st to the 4 th. process is the first step towards implementing self-healing functionality and detecting such that the self-healing functionality is triggered after the fault classification process gas is completed and a viable mitigation solution is found within the fault-solving library. This paper adopts a narrative research method within the qualitative methodology, using existing literature to highlight theories, machine-learning algorithms, and network architectures for implementing self-healing functionality, which can then be deployed to protect the security of cyber-physical systems. The paper surveys existing literature and shows the research areas where similar systems have been implemented and gaps still exist, intending to aid future studies. The goal and objectives of this paper encompass the following four points, which aim to contribute to enhancing knowledge in the field of study:  
* This paper aims to enhance knowledge by highlighting current trends in the area of study;
* The main objective of this paper is to identify the latest machine-learning tools, methods, and algorithms for integrating self-healing functionality into cyber-physical systems;
* The self-healing capability of cyber-physical systems will be evaluated concerning state-of-the-art techniques, and machine-learning tools and methods in implementing self-healing functions will be explored;
* The existing literature will be critically reviewed to identify current tools, methods, algorithms, classification models, frameworks, networks, and architectures currently deployed for a self-healing approach. The implications of the existing approaches for future research are discussed, emphasising how the literature review and findings can contribute to advancing future experiments. This paper's structure includes sections on self-healing theories, self-healing for cyber-physical systems, self-healing methods, an analytical comparison of promising approaches, and a critical discussion of presented theories and techniques. The taxonomy of the literature is summarised in Table 1.