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008			220322s2022 sz a s |1|| 0|eng d
020			_a9783030989781
024	7		_a10.1007/978-3-030-98978-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
245	0	0	_aMachine Learning for Networking : _b4th International Conference, MLN 2021, Virtual Event, December 1-3, 2021, Proceedings _cedited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (X, 161 páginas) _b69 ilustraciones, 50 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático _vCongresos y asambleas
650		7	_2embne _9678664 _aRedes neuronales artificiales _vCongresos y asambleas
650		7	_2embne _9249221 _aRedes informáticas _xMedidas de seguridad _vCongresos y asambleas
700	1		_aRenault, Éric _eeditor literario _0(orcid)0000-0003-1011-8347 _1https://orcid.org/0000-0003-1011-8347 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBoumerdassi, Selma _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMühlethaler, Paul _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030989774
776	0	8	_iPrinted edition: _z9783030989798
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-98978-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _esc _zSI