## 3 Synopsis of KDN Paradigm
### Detailed KDN Architecture
#### 3.2.1 Knowledge/Cognitive Layer  
A conceptual layer called the knowledge layer is in charge of producing, combining, and distributing knowledge over a network. This layer enables network managers to see issues and address them before they arise or worsen inside the network, maximize network functionality in response to evolving demands, and take preventative measures to reduce risks. The knowledge and/or rules generated by the knowledge layer may be used to identify and fix network problems, setup the network with the least amount of user involvement, find malicious attacks or anomalies, etc. Three sub-layers make up the knowledge layer. Employing data and information, the knowledge creation layer creates knowledge utilizing algorithmic-based or artificial intelligence techniques. The Resource-Description Framework (RDF) modeling language may be utilized for modeling the knowledge generated by the knowledge-generating layer. Resource, property, and value are the triad that RDF provides to express knowledge. Resources and values are distinguished by uniform resource identifiers, while properties show the connections between resources and values [161]. Rule-based techniques often entail the use of an algebraic framework to explain the fresh data [162] or the use of the data's internal connections [163] to produce knowledge. Simple logic or data fusion are two other pragmatic ways of producing knowledge [164]. An ontology editor is implemented in the knowledge composition layer to combine created knowledge and universal knowledge (pre-existing knowledge in the knowledge base) to create assembled knowledge, which may be utilized to build rules via integration with user intent, as shown in Figure 7. Furthermore, by employing an ontology vocabulary to designate organizational structures, groups, connections, and attributes within the fields, an ontology editor may be utilized to construct the ontology's architecture [165]. In order to provide the structure and allow for expressing and modifying knowledge using a computer-understandable format, five primary knowledge modeling languageshave been introduced for KDNs: Knowledge-Interchange Format (KIF) [166], Ontology-Inference Layer (OIL) [167], Resource-Description Framework (RDF) [161], RDF-Schema (RDFS) [168], and Web-Ontology Language (OWL) [169]. Additionally, in order to create fresh rules using a rule creator to create rules that may be utilized by other layers, the knowledge composer layer creates innovative rules by comparing the requirements of the application with the combined knowledge [64]. A rule creator is often realized as a rule-based model using a computer language like Java or Lisp [170]. The rules produced from the rule creator ought to be expressed in a universal language so that other layers can comprehend them. A specialized rule language, such as Rule-Markup Language (RuleML) [171], Rule-Interchange Format (RIF) [172], or Semantic-Web Rule Language (SWRL) [173], may perhaps be utilized to express the created rules. The knowledge management and dissemination layer includes a repository of knowledge known as the knowledge base to store rules/knowledge and utilizes appropriate protocols/languages to enter, modify, eliminate, and share rules/knowledge [34]. Thus,  
Figure 7: Detailed block schematic for the layered structure of KDN with interfaces, sub-layers, and flows. knowledge produced through knowledge creation models, combined knowledge created using ontology editors, rules created by rule creators, network data that have been gathered, and messages from the control layer make up this knowledge base in this sub-layer. Additionally, in order to analyze the rules or knowledge communicated by the knowledge dissemination sub-layer and carry out the operations, other layers must include a reasoning (rule) engine like RETE [174], Bossam [175], Jess [176], Drools [177], etc. A rule engine applies rules or draws conclusions from understanding, then decides, depending on how well the rules and knowledge were applied.