###### Abstract
An intelligent hybrid clinical diagnostic system using particle swarm optimization and back propagation neural networks has been utilized to detect prostate cancer using medical records with high accuracy to aid in clinical decision-making [59]. Similarly, deep convolutional neural networks have been leveraged to detect subtypes of acute lymphoblastic leukemia using blood smear images to generate knowledge regarding the disease from benign cases and determine the appropriate treatment technique [60]. The KDN idea continues to be relatively fresh, and there are not many standards or techniques for communication within the main planes [61]. In contrast to SDN, KDN features an extra administration (management) layer and a cognitive (knowledge) layer that are conceptually distinct from the operation of the control layer. The cognitive layer uses knowledge creation frameworks to analyze all the information and data gathered from the management and control layers and to create regulations and intelligence that are then provided to the application, administration, and control layers. By automatically designing flows according to an evaluation of recent data and immediate information gathered from the network, KDN has been used to enhance the efficiency of networks [62]. KDN transforms classic SDN's humanly programmed control plane operations into a self-executing, dynamic control plane that creates rules based on AI-/ML-generated understanding [63]. KDN, whose network actions are driven by AI-/ML-generated knowledge and optimization techniques, has a higher efficiency than traditional networking, in which network device actions are explicitly defined by the network administrators, thus not being able to adapt to dynamic network conditions. In contrast to traditional networking, KDN has high automation, high scalability, high security, and a low operational cost. However, KDN is more complex due to additional layers; thus, it incurs a high implementation cost compared to traditional networking [64]. The survey in [65] explores the application of blockchain in SDN for security and protection. A similar survey presents blockchain applications in SDN for security and protection, albeit with a broader perspective and future avenues [66]. Furthermore, another review article presents the application of blockchain in SDN in both security and non-security fields and further investigates the challenges of the applications [67]. The review paper in [68] presents the fusion of blockchain and ML for 5G and beyond IoT networks. Similarly, review papers [69; 70; 71] review the blockchain and machine learning integrated frameworks for wireless sensor networks and smart grids, with an emphasis on security and privacy. Different from the previously mentioned surveys, our survey involves reviewing the application of blockchain technology in knowledge-defined networking. Knowledge-defined networking can be expressed as the artificial intelligence-/machine-learning-/knowledge-based extension of the SDN paradigm, which encapsulates all types of knowledge-driven networks such as intelligent smart grids, intelligent wireless sensor networks, intelligent Internet of Things, intelligent vehicular networks, intelligent optical networks, intelligent medical networks, etc. Therefore, compared to surveys on blockchain in generic SDN [65; 66; 67], our survey is for a special futuristic case of SDN, where knowledge is generated using machine learning/artificial intelligence/fuzzy logic/meta-heuristic techniques for blockchain applications. Moreover, compared to previous surveys [68; 69; 70; 71], our survey has a broad perspective, having generic knowledge-driven (not only limited to machine learning, but also addressing other heuristic knowledge-generation techniques such as optimization, meta-heuristics, fuzzy logic, etc.) network applications without being bound to the security and privacy applications of a special network category of knowledge-based networks.