Titlebook: Machine Learning for Networking; 4th International Co Éric Renault,Selma Boumerdassi,Paul Mühlethaler Conference proceedings 2022 Springer

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Cross Inference of Throughput Profiles Using Micro Kernel Network Method,network profiles. In particular, we propose a micro Kernel Network (mKN) that provides baseline throughput measurements on the host running Mininet emulations, which are used to learn a regression map that converts them to the corresponding testbed measurement estimates. Once initially learned, this

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DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Cluhallow learning model, k-means++. The variational autoencoder is used to compress and extract the most useful features from the flow inputs. The compressed and extracted features then serve as input-output pairs to k-means++. The k-means++ explores the structure hidden in these features and then use

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Deep Reinforcement Learning for Cost-Effective Controller Placement in Software-Defined Multihop Wising DRL to the CPP in SDMWN for a given number of controllers is investigated to minimize the generated control overhead referred to as the network cost. The results show that the adapted DRL is able to find controller placements and assign the controllers to devices such that the obtained network

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620646.jpg

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https://doi.org/10.1007/978-3-030-98978-1artificial intelligence; communication systems; computer crime; computer networks; computer security; com