###### Abstract  
The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system's stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber-physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber-physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber-physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber-physical systems. cyber-physical system; cybersecurity; threat tolerance; self-healing; intrusion detection; machine-learning algorithms +
Footnote †: journal: _Future Internet_  
capabilities can improve the performance and robustness of CPSs, they also face several vulnerabilities, threats, and challenges that need to be addressed [3]. These include hardware and software component vulnerabilities that may be susceptible to cyber-attacks and other threats, compromising the self-healing process [4]. The lack of standardisation in self-healing mechanisms and technologies creates system interoperability issues, leading to vulnerabilities and integration challenges [2]. The complexity of CPSs, with multiple interconnected components, poses difficulties in identifying and diagnosing faults, making it challenging to implement effective self-healing mechanisms [5]. Human error during system design, implementation, and maintenance can create vulnerabilities and compromise the self-healing capabilities of the system [2]. The lack of visibility into CPS self-healing systems can also hinder fault identification and compromise system operations [6]. CPS self-healing systems are also vulnerable to malicious attacks, including denial-of-service attacks, malware, and hacking, which can compromise the system's integrity and availability [7]. Considering that CPS self-healing systems are vital for critical infrastructure, such as transportation systems and power grids, failures or vulnerabilities in these systems can have severe safety implications [8]. Addressing these vulnerabilities, threats, and challenges is essential to ensure the security, reliability, and safety of critical infrastructure supported by self-healing capabilities in CPSs [2]. The increased adoption of digital systems in conducting human socioeconomic development affairs concerning business, manufacturing, healthcare provisions, and government services comes with the attendant risk of increased threats to computer systems and networks.