# A Comprehensive Survey on Knowledge-Defined Networking
## 3 Knowledge Plane
### Knowledge Composition Plane
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline
**Task** & **Learning Class** & **Learning Technique** & **Special Purpose** & **Performance** \\ \hline \multirow{9}{*}{\begin{tabular}{} \end{tabular} } & Deep neural network & TC for energy efficiency optimization [106] & 47.71\% energy consumption reduction, good load balancing \\ \cline{2-4}  & Decision tree & Online TC using flow-level features [122] & 97.92\% accuracy, 7500+ Million Classes \\ \cline{2-4}  & DT in Hadoop platform & Big data traffic classification [123] & Faster and accurate than C4.5 decision tree \\ \cline{2-4}  & Supervised ML & Support Vector Machines & Application-/service-wise traffic classification [128] & Classify 28 applications with 85.98\% accuracy \\ \cline{2-4}  & Support Vector Machines & Traffic classification using traffic statistics [130] & Accuracy over 95\%, 4\% F1-score for attacks \\ \cline{2-4}  & Random forest & Internet protocol traffic classification [135] & Classification accuracy varied 73–96\% for different apps \\ \cline{2-4}  & Random forest & Classify application QoS from switch statistics [136] & Prediction error less than 10\% \\ \cline{2-4}  & Semi-supervised ML & Generative adversarial networks & ByteSGAN-Encrypted traffic classification [158] & Accuracy more than 90\% and better than CNN \\ \cline{2-4}  & Self-learning & Application-wise traffic classification [165] & 20\% LD: Accuracy—60\%. High recall, similar precision wrt. supervised learning \\ \cline{2-4}  & Integrated ML & Ensemble learning with RF and GB & VPN (secure) network traffic classification [179] & 93.80\% accuracy, Precision—91.80\%, recall—96.20\%, F1-score—94\% \\ \hline \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & Deep neural network & Detect link failures [108] & 85\% accuracy in identification of failures \\ \cline{2-4}  & Supervised ML & LSTM-based DNN & Detect link handover failures in advance [109] & 99\% TPR, predict a link failure before 1-2 s \\ \cline{2-4}  & K nearest neighbor & Harmful long-term load & Accuracy—96.2\%, Precision—91.2\%, F1-score—94.6\% \\ \hline \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & GRU-DNN & Temporal-aware service QoS & Superior accuracy wrt. other approaches \\ \cline{2-4}  & Supervised ML & DNN & Multiple attributes QoS & Low mean absolute error wrt. other approaches \\ \cline{2-4}  & Supervised ML & DNN & Multiple attributes QoS & Low mean absolute error wrt. other approaches \\ \cline{2-4}  & Prediction & K nearest neighbor & Predict QoE in video data [143] & k = 1, Pearson Correlation Coefficient—0.75, long testing time \\ \cline{2-4}  & Semi-supervised ML & Laplacian SVM & Classifies network traffic based on QoS requirements [163]. & Accuracy exceeds 90\% and better than K-means \\ \hline \
\begin{tabular}{} \end{tabular} & Supervised ML & K nearest neighbor & Adaptive cell selection for mm Wave HetNets [139] & Better handovers—45.83\%, throughput—17.2\%, EE—16.7\% \\ \hline
\begin{tabular}{} \end{tabular} & Semi-super. ML & Conditional Generative adversarial networks & CGAN augment data used in creating network slices [159] & Accuracy range in 0.40–0.65 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._not be able to be controlled by this warning signal alone. This knowledge may have to be combined with other knowledge such as collision warnings received from other sensors such as proximity sensors, warnings received from RSUs, output from another knowledge model (a trained ML algorithm), etc. in order to decide the cruise control value. The composed knowledge by the ontology editor can be represented and modeled in numerous ways, which can be selected based on the type of knowledge and design requirements.