## 3 Self-Healing for Cyber-Physical Systems
Provision of self-configuration resources by reinstalling newer versions of obsolete dependencies of the system's software and offers management of errors through self-healing;
2. Automatically schedules resource provisioning and optimises QoS without the need for human intervention;
3. Provides algorithms for four-phased approaches of monitoring, analysis, planning, and execution of the QoS values. These four phases are triggered through corresponding alerts to aid the preservation of the system's efficiency;
4. Reduces the breach of service level agreement (SLA) and increases the QoS expectation of the user by improving the availability and reliability of services. A prominent issue in current research is the ability of systems to identify \"zero-day\" or never-before-seen anomaly events intelligently. The proposal presented by [33] suggests utilising a knowledge-based algorithm to construct an intrusion detection system (IDS) that effectively prevents power grid fault line intrusion. Experiments were conducted within a testbed of a six-bus mesh network modelled to identify fault events within the system and concurrently perform mitigating actions initiated by [33] and proposed as a novel protocol. The proposed protocol is referred to as autonomous isolation strategies. The strategies involve rerouting power flow displacements within the power grid once a threat intrusion is detected. Simulations during the experiment were conducted using Power World Simulator, MATLAB, and SimAuto (a fault detection platform). As noted in [33], the experiment result shows that MATLAB extracts network parameters. Then, self-healing strategies are triggered by rerouting network processes to other distribution areas, providing stability to the system. The self-healing approach, started by the knowledge-based algorithm, continues concurrently until all the overloading lines on the grid are cleared and all effects of the system's threat eradicated. The guidelines of supervised learning for the knowledge-based algorithm as related to the electric power network are listed as having the following characteristics in [33]:  
1. Detection of overloaded transmission lines in the power network;
2. Identify buses that have overloaded transmission lines connected to them;3. Identification of the busbar that has the highest reserve capacity and that can then serve as a viable option for a power restoration strategy;
4. Identification of the nearest distribution generator to the overloaded transmission line;
5. Identification of the termination point of the overloaded lines;
6. Establishment of line connection using the references of the reserve busbar index. Other anomaly detection systems for network diagnosis are proposed in previous studies, such as ARCD by [34], which uses data logs collected from large-scale monitoring systems to identify root causes of problems in a cellular network. An experiment by [34] identified that ARCD systems achieved rate levels above 90% in terms of anomaly detection accuracy rate and detection rate. The drive towards an automatic diagnosis of computer systems failures in mobile cellular networks is propelled by the industry's need for efficient means of identifying problems within the network. Interestingly, Ref. [35] noted that mobile network operators spend a quarter of their revenues on network maintenance, and a drive towards maintenance automation will drive down costs. A solution that relies on random tree forest (RF), convolutional neural network (CNN), and neuromorphic deep-learning module to perform fault diagnostics were proposed by [35]. The proposal uses an RSRP map of fault-generated images to provide an AI-based fault diagnostic solution. The impact of fault diagnostic solutions is noticeable in reducing costs and improving the end user's overall quality of service (QoS). Experiments during research by [35] show that the proposed system could identify all the faults fed through the image datasets.