Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Semi-supervised learning in SDN <s> In Software Defined Network (SDN) environment, controller has to compute and install routing strategy for each new flow, leading to a lot of computation and communication burden in both controller and data planes. In this background, intelligent routing pre-design mechanism is regarded to be an important approach for routing efficiency enhancement. This paper investigates and proposes efficient SDN routing pre-design solution in three aspects: flow feature extraction, requirement prediction and route selection. First, we analyze and extract data packet and association features from user history data, apply these features into semi-supervised clustering algorithm for efficient data classification, analysis and feature extraction. After that, flow service requirement could be predicted through extraction of user, flow and data plane load features and implementation of supervised classification algorithm. Furthermore, we propose corresponding handling strategies related to data plane topology, flow forwarding and multi-constraint weight assignment, and proposes personalized routing selection mechanism. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Semi-supervised learning in SDN <s> In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, andthe inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Semi-supervised learning in SDN <s> Improvement guarantees for semi-supervised classifiers can currently only be given under restrictive conditions on the data. We propose a general way to perform semi-supervised parameter estimation for likelihood-based classifiers for which, on the full training set, the estimates are never worse than the supervised solution in terms of the log-likelihood. We argue, moreover, that we may expect these solutions to really improve upon the supervised classifier in particular cases. In a worked-out example for LDA, we take it one step further and essentially prove that its semi-supervised version is strictly better than its supervised counterpart. The two new concepts that form the core of our estimation principle are contrast and pessimism. The former refers to the fact that our objective function takes the supervised estimates into account, enabling the semi-supervised solution to explicitly control the potential improvements over this estimate. The latter refers to the fact that our estimates are conservative and therefore resilient to whatever form the true labeling of the unlabeled data takes on. Experiments demonstrate the improvements in terms of both the log-likelihood and the classification error rate on independent test sets. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Semi-supervised learning in SDN <s> Many countries have set up E-health monitoring systems to relieve the pressure on care service with continuous monitoring services. Wireless software-defined-network (SDN) enabled massive machine-type communications (mMTC) network architecture is agreed as a promising candidate to support feasible management on wireless monitoring devices in E-health system. The existing anomaly detection scheme (ADS) in SDN-mMTC involves periodically traffic uploading for monitoring and model re-training, leading to big scalability issues in E-health system. Therefore, this paper proposes a Localized Evolving Semi-supervised Learning Based Anomaly detection scheme (LESLA), which employs the idea of offline training plus online testing to allow running localized ADS to be feasible on wireless mMTC devices. Besides, LESLA adopts semi-supervised learning together with contrastive pessimistic likelihood estimation (CPLE) to enable the self-evolving during anomaly detection. Such design results in promising good performance even in the case of outlier or infrequent model renewal. Furthermore, CPLE_LESLA scheme is proposed to further improve the performance of LESLA. The theoretical analysis and the simulation are carried out to demonstrate the efficiency and advantages of LESLA scheme in mMTC. <s> BIB004
Semi-supervised learning BIB002 BIB001  BIB004 were also used in SDN, but much less common compared with other learning approaches. The research was focused on traffic classification BIB002  , routing BIB001 and intrusion detection  . Wang et al. BIB002 proposed a new framework for QoS-aware traffic classification based on semi-supervised learning. This approach can classify the network traffic according to the QoS requirements. The system allows achieving deep packet inspection (DPI) and semi-supervised learning based on Laplacian SVM. The Laplacian SVM approach outperformed a previous semi-supervised approach based on k-means classifier. Chen and Zheng BIB001 introduced an efficient routing predesign solution based on semi-supervised approach. The study suggested using an appropriate clustering algorithm such as Gaussian mixture model and k-means clustering for feature extraction. Thereafter, a supervised classification approach such as extreme learning machine (ELM) can be used for flow demand forecasting. The authors also suggested using an adaptive multipath routing approach based on analytic hierarchy process (AHP) for handling to elephant flows according different constraint factor weights. Li et al.  proposed a new method for fine-grained traffic classification based on semi-supervised approach called nearest application based cluster classifier (NACC). Unlike traditional methods, which use one feature vectors, this algorithm constructs a matrix with several cluster centroids based on k-means clustering to represent the application. The algorithm uses a small number of labelled flows to build a supervised dataset. Then collects the unlabelled flows to be merged with previously collected dataset, based on investigating the correlated flows, which is used to map an application to different clusters. The experimental results showed a good identification accuracy reaching 90%. Wang et al. BIB004 introduced an intrusion detection method based on semi-supervised approach for wireless SDN-based ehealth monitoring systems. The proposed system employed the concept of off-line training and on-line testing to allow running localized intrusion detection on wireless massive machinetype communications (mMTC) devices. Their system adopts semi-supervised learning on the basis of modified contrastive pessimistic likelihood estimation (CPLE), in which they replace the maximization calculation by a relaxation function. CPLE BIB003 performs semi-supervised parameter estimation for likelihood-based classifiers. The proposed modified CPLE outperformed Naive Bayes, SVM, DNN, self-training (semisupervised approach) and the original CPLE based on the experiments conducted on NSL-KDD dataset.