###### Abstract
For example, based on security events or behavior-based data collected, when the knowledge plane categorizes a certain set of network devices as malicious devices, the control plane can enforce the routing application policy by updating all flow tables in the network and setting flow rules such that packets are not forwarded through the malicious devices. Thus, when implementing a KDN, detection data from various forwarding devices is employed to develop insight into the general control setting [33]. The key roles of the knowledge plane in KDN are knowledge generation, knowledge compilation, and knowledge dissemination [34]. As another option, KDN has been put forth by academics as a framework for implementing artificial intelligence (AI) in SDN [35]. Knowledge-based networking, also known as the KDN paradigm, combines data and information to create understanding by employing AI models or rule-based models, such that KDN represents knowledge-based networking in all types of networks [36]. Despite the fact that the idea of a knowledge plane was first put forward in [37] almost 20 years ago, KDN has lately attracted interest because of the challenges associated with moving directly from regular networks to KDN and current developments in artificial intelligence. For KDN's security concerns, Machine Learning (ML) has been employed for identifying breaches [38], detecting service denial assaults [39], and identifying abnormal behavior [40]. In [41], machine learning has been used to predict control traffic to aid in controller virtualization and control channel isolation. Furthermore, researchers in the study [42] utilized deep neural networks to identify communication patterns in nearby automobiles that reduced the number of packet crashes. Moreover, in the KDN, ML has additionally been employed to classify flow [43] and packets [44], forecast connection stability [45], determine the optimum pathways using link lifetime and delay prediction [46], and perform other tasks. Artificial intelligence has been utilized for automotive networks that employ the idea of KDN to anticipate Vehicle-to-Infrastructure (V2I) connection reliability [47], identify service denial incidents [48], and locate paths based on faith [49], among other things. Additionally, knowledge for KDNs has been generated using fuzzy reasoning, meta-rules, and artificial intelligence [50]. Through network orchestration of operations chores, KDN may decrease the requirement for human involvement, save operating expenses, and boost energy savings [51]. In order to achieve the idea of an autonomous vehicular network, the KDN principle has been applied for network surveillance as a feedback control system [52]. Likewise, reinforcement learning with deep learning can potentially be applied for autonomous packet forwarding in KDNs to acquire knowledge from experience while carrying out surveillance of the network to understand how the environment interacts [53]. Additionally, a self-contained packet forwarding method that uses deep machine learning in a self-governing KDN to determine the most trustworthy pathways on request has been studied in [54]. Furthermore, an autonomous driving system utilizing graph-based neural networks that chooses the best route for responsive traffic operating and service function linking has been researched in [55], taking advantage of automation coming from knowledge development in KDN. In [56], an artificial intelligence-based approach to identifying huge striking fluxes was examined. On top of that, fifth-generation KDNs [57] have employed artificial intelligence for audiovisual traffic categorization. Machine learning--specifically, deep learning--has been extensively applied in healthcare networks for medical image analysis, etc. to generate knowledge regarding disease diagnosis with high classification performance [58].