Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Self-organizing maps (SOM) <s> The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. > <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Self-organizing maps (SOM) <s> Abstract Software-Defined Networking (SDN) is a new programmable networking model that features the detachment of control and data planes. In this network, the network brain is an SDN controller that is used to centrally monitor and control the data plane based on the OpenFlow protocol and applications located in the application layer. In recent years, a vast number of issues relating to security have been seriously debated for this networking paradigm, especially the large-scale model. In particular, flooding attacks have been on the rise, providing great challenges for the SDN architecture to cope with. In this paper, we present a novel mechanism using the Self-Organizing Map (SOM) application to solve the performance bottleneck and overload problems for the upper layers in a large-sized SDN in case of flooding attacks. Our proposed approach integrates a Distributed Self-Organizing Map (DSOM) system to OpenFlow Switches instead of using a standalone SOM. By exploiting SDN advantages, such as flexibility and overhead reduction, we implement and test both a DSOM system and a single SOM system on multi-criteria to compare the performance of our introduced system. Our experimental results show that the DSOM solution can effectively detect abnormal traffic, solve bottleneck problems and increase the system reaction speed to attack traffic, while presenting a smaller overhead to the network system. <s> BIB002
Self-organizing maps (SOM) BIB001 BIB001 , is a well-known unsupervised learning approach in artificial neural networks, which maps high-dimensional distribution to a low-dimensional representation called a SOM map BIB002 . SOMs have proven to be successful in various pattern recognition tasks including very noisy signals BIB001 . The training process in SOM, builds and reorganizes the map using input data. Thereafter, it classifies a new input vector based on finding its winning neuron or node in the map BIB002 .