Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> Packet header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> Network intrusion detection is an important technique in computer security. However, the performance of existing intrusion detection systems (IDSs) is unsatisfactory since new attacks are constantly developed and the speed of network traffic volumes increases fast. To improve the performance of IDSs both in accuracy and speed, this paper proposes a novel adaptive intrusion detection method based on principal component analysis (PCA) and support vector machines (SVMs). By making use of PCA, the dimension of network data patterns is reduced significantly. The multi-class SVMs are employed to construct classification models based on training data processed by PCA. Due to the generalization ability of SVMs, the proposed method has good classification performance without tedious parameter tuning. Dimension reduction using PCA may improve accuracy further. The method is also superior to SVMs without PCA in fast training and detection speed. Experimental results on KDD-Cup99 intrusion detection data illustrate the effectiveness of the proposed method. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to a sensor node fault detection. Recurrent neural networks (RNNs) are used to model a sensor node, its dynamics, and interconnections with other sensor network nodes. The modeling approach is used for sensor node identification and fault detection. The input to the neural network is chosen to include delayed output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of backpropagation-type neural network. The input to the neural network and topology of the network are based on a general nonlinear dynamic sensor model. A simulation example has demonstrated effectiveness of the proposed scheme. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> The task of network management and monitoring relies on an accurate characterization of network traffic generated by different applications and network protocols. We employ three supervised machine learning (ML) algorithms, Bayesian Networks, Decision Trees and Multilayer Perceptrons for the flow-based classification of six different types of Internet traffic including peer-to-peer (P2P) and content delivery (Akamai) traffic. The dependency of the traffic classification performance on the amount and composition of training data is investigated followed by experiments that show that ML algorithms such as Bayesian Networks and Decision Trees are suitable for Internet traffic flow classification at a high speed, and prove to be robust with respect to applications that dynamically change their source ports. Finally, the importance of correctly classified training instances is highlighted by an experiment that is conducted with wrongly labeled training data. <s> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> The appearance of new emerging multimedia services have created new challenges for cloud service providers, which have to react quickly to end-users experience and offer a better Quality of Service (QoS). Cloud service providers should use such an intelligent system that can classify, analyze, and adapt to the collected information in an efficient way to satisfy end-users' experience. This paper investigates how different factors contributing the Quality of Experience (QoE), in the context of video streaming delivery over cloud networks. Important parameters which influence the QoE are: network parameters, characteristics of videos, terminal characteristics and types of users' profiles. We describe different methods that are often used to collect QoE datasets in the form of a Mean Opinion Score (MOS). Machine Learning (ML) methods are then used to classify a preliminary QoE dataset collected using these methods. We evaluate six classifiers and determine the most suitable one for the task of QoS/QoE correlation. <s> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> The rapid growth of video traffic in cellular networks is a crucial issue to be addressed by mobile operators. An emerging and promising trend in this regard is the development of solutions that aim at maximizing the Quality of Experience (QoE) of the end users. However, predicting the QoE perceived by the users in different conditions remains a major challenge. In this paper, we propose a machine learning approach to support QoE-based Video Admission Control (VAC) and Resource Management (RM) algorithms. More specifically, we develop a learning system that can automatically extract the quality-rate characteristics of unknown video sequences from the size of H.264-encoded video frames. Our approach combines unsupervised feature learning with supervised classification techniques, thereby providing an efficient and scalable way to estimate the QoE parameters that characterize each video. This QoE characterization is then used to manage simultaneous video transmissions through a shared channel in order to guarantee a minimum quality level to the final users. Simulation results show that the proposed learning-based QoE classification of video sequences outperforms commonly deployed off-line video analysis techniques and that the QoE-based VAC and RM algorithms outperform standard content-agnostic strategies. <s> BIB006 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> Swarm Intelligence techniques have been widely used in the science and engineer domains such as Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). It is a relatively novel and promising field, focusing on the adaptation of collective behaviors of various natural creatures like ants, fish, birds, and honey bees, and a large number of routing protocols for WSNs have been developed according to the inspiration from the foraging behaviors of these species. In this paper, we first discuss the general principle of swarm intelligence and survey the research efforts on these SI based protocols according to various promising meta-heuristics. In the second part of the paper, we present the properties of termite colony optimization and fission-fusion social structure based spider monkey optimization based clustering in WSNs. Last, we conclude the paper with a comparative analysis, pointing out the fundamental issues and potential future directions. <s> BIB007 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> ARTIFICIAL INTELLIGENCE IN SDN <s> Extending the current Internet with interconnected objects and devices and their virtual representation has been a growing trend in recent years. The Internet of Things (IoT) contribution is in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge for the benefit of society. Benefit due to the service controlled by communication between objects is now being increased by people who use these services in real life. The sensors are deployed to monitor one or more events in an unattended environment. A large number of the event data will be generated over a period of time in IoT. Hence, the load balancing protocol is critical considerations in the design of IoT. Therefore, we propose an agent Loadbot that measures network load and process structural configuration by analyzing a large amount of user data and network load, and applying Deep Learning's Deep Belief Network method in order to achieve efficient load balancing in IoT. Also, we propose an agent Balancebot that processes a neural load prediction algorithm based on Deep Learning's Q-learning method and neural prior ensemble. We address the key functions for our proposed scheme and simulate the efficiency of our proposed scheme using mathematical analysis. <s> BIB008
AI and ML approaches have been widely used for solving various problems such as routing BIB007 , traffic classification BIB004 , flow clustering BIB001 intrusion detection BIB002 , load balancing BIB008 , fault detection BIB003 , quality of service (QoS) and quality of experience (QoE) optimization BIB005 , admission control and resource allocation BIB006 . However, in the era of SDN the role of AI was significantly increased due to the huge efforts made by industry and research community. Recent studies have shown a strong tendency of research community towards adoption of AI approaches in SDNs. It is worth mentioning that ML, meta-heuristics and fuzzy systems were the most common approaches for solving various networking related problems. We investigate the application areas of each approach in the SDN paradigm separately as will be shown in the reset of this study.