## 4 Self-Healing Approaches
### Self-Healing Models and Frameworks  
A self-organising network was presented by [10], called the self-organised network (SON) experiment framework, with which self-healing functionality is implemented and demonstrated using data from actual network instances and live integrations. The SON experiment framework is a tool developed by [PERSON] [10]. It is implemented in R language and has a user interface to visualise anomaly detection and its diagnosis process. Further research includes using the transfer-learning method to investigate the remediation aspect of the self-healing system. A reoccurring theme is addressed in the literature relating to self-healing methods, and [36], in describing self-healing software techniques, noted that the techniques are modelled after the observer orient decide act (CODA) feedback loop. The OODA model identifies where to apply protection by observing the system's behaviour. The system is monitored to detect the fault and determine fault parameters, such as the type of fault, the input or the sequence of events that led to the input, the approximate areas of the system that is affected by the defect, and the information that may be useful in mitigating the fault. The self-healing mechanism in terms of OODA is more appealing than the traditional defence mechanism, which prioritises the termination of attack processes and restarting the system in the event of an attack. The self-healing tool succeeds by preventing code injection or the misused of legitimate code, rather than the traditional defence method, which may cause systems fault to persist even after the attack process has been terminated and the system restarted. Self-healing functionality implementation has at its core anomaly detection, and once an anomaly is detected using the various methods that are presently available, such as network intrusion detection system (NIDS) and or host-based intrusion detection systems (HIDS), then performing remediation through triggered actions becomes necessary to protect the system and realise the self-healing function. A self-healing system must encompass resilience through its ability to take corrective measures that return the system to its routine or default state after system failure or attack. Systems that are capable of remedial actions against losses and or attacks from outside sources were proposed by [PERSON] in [37]. However, the proposal cannot trigger automatic remediation to mitigate attack events in real time but still requires human intervention to perform the remediation process. Therefore, a self-healing framework that is automatic and uses collaborative host-based learning to incorporate a self-healing mechanism into the Internet of Things (IoT) devices was proposed by [PERSON] in [37] in a study where a lightweight HIDS designed for IoT was deployed. The authors of [33] described the conventional self-restoration within the electrical systems concept as a system that can automatically reconfigure itself to achieve repossession during an unexpected power disruption event. Automation within the power grid is the concept of autonomously restoring proceedings that are impacted by power failures and restoration of power supply through redirecting power flow from stable lines to the fault-affected areas instantaneously. The automation principle facilitates reforming the existing power grid to dynamically respond to fault detection, applying deterministic isolation techniques and executing reroute operations in real-time. An example is shown in a framework demonstrated by [37] in the form of a tool for detecting anomaly events on cyber-physical systems. The system sends alerts through the HIDS and triggers the best possible remediation action, neutralising the attack effects and returning the IoT devices to their normal state. An auto-remediation model is deployed, which uses an evolutionary-computation algorithm built to imitate the functions of the natural process of evolution and in which the fittest are likely to survive through a process like natural selection.