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024 7 _a10.1007/978-3-030-98978-1
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245 1 0 _aMachine Learning for Networking
_h[electronic resource] :
_b4th International Conference, MLN 2021, Virtual Event, December 1–3, 2021, Proceedings /
_cedited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aX, 161 p. 69 illus., 50 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13175
505 0 _aEvaluation of Machine Learning Methods for Image Classification: A Case Study of Facility Surface Damage -- One-Dimensional Convolutional Neural Network for Detection and Mitigation of DDoS Attacks in SDN -- Multi-Armed Bandit-based Channel Hopping: Implementation on Embedded Devices -- Cross Inference of Throughput Profiles Using Micro Kernel Network Method -- Machine Learning Models for Malicious Traffic Detection in IoT networks /IoT-23 dataset -- Application and Mitigation of the Evasion Attack against a Deep Learning Based IDS for Io -- DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering -- Unsupervised Anomaly Detection using a new Knowledge Graph Model for Network Activity and Events -- Deep Reinforcement Learning for Cost-Effective Controller Placement in Software-Defined Multihop Wireless Networking -- Distance estimation using LORA and neural networks.
520 _aThis book constitutes the thoroughly refereed proceedings of the 4th International Conference on Machine Learning for Networking, MLN 2021, held in Paris, France, in December 2021. The 10 revised full papers included in the volume were carefully reviewed and selected from 30 submissions. They present and discuss new trends in in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G systems, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, resource allocation, energy-aware communications, software-defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, and underwater sensor networks.
650 0 _aData mining.
650 0 _aComputer networks .
650 0 _aApplication software.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aComputer Communication Networks.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aRenault, Éric.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBoumerdassi, Selma.
_eeditor.
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700 1 _aMühlethaler, Paul.
_eeditor.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783030989798
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13175
856 4 0 _uhttps://doi.org/10.1007/978-3-030-98978-1
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