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245 1 0 _aMachine Learning for Networking
_h[electronic resource] :
_b6th International Conference, MLN 2023, Paris, France, November 28-30, 2023, Revised Selected Papers /
_cedited by Éric Renault, Selma Boumerdassi, Paul Mühlethaler.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 286 p. 116 illus., 90 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14525
505 0 _a -- Machine Learning for IoT Devices Security Reinforcement. -- All Attentive Deep Conditional Graph Generation for Wireless Network Topology Optimization. -- Enhancing Social Media Profile Authenticity Detection A Bio Inspired Algorithm Approach. -- Deep Learning Based Detection of Suspicious Activity in Outdoor Home Surveillance. -- Detecting Abnormal Authentication Delays in Identity and Access Management using Machine Learning. -- SIP DDoS SIP Framework for DDoS Intrusion Detection based on Recurrent Neural Networks. -- Deep Reinforcement Learning for multiobjective Scheduling in Industry 5.0 Reconfigurable Manufacturing Systems. -- Toward a digital twin IoT for the validation of AI algorithms in smart-city applications. -- Data Summarization for Federated Learning. -- ML Comparison Countermeasure prediction using radio internal metrics for BLE radio. -- Towards to Road Profiling with Cooperative Intelligent TransportSystems. -- Study of Masquerade Attack in VANETs with machine learning. -- Detecting Virtual Harassment in Social Media Using Machine Learning. -- Leverage data security policies complexity for users an end to end storage service management in the Cloud based on ABAC attributes. -- Machine Learning to Model the Risk of Alteration of historical buildings. -- A novel Image Encryption Technique using Modified Grain. -- Transformation Network Model for Ear Recognition. -- Cybersecurity analytics: Toward an efficient ML-based Network Intrusion Detection System (NIDS).
520 _aThis book constitutes the refereed proceedings of the 6th International Conference on Machine Learning for Networking, MLN 2023, held in Paris, France, during November 28-30, 2023. The 18 full papers included in this book were carefully reviewed and selected from 34 submissions. The conference aims at providing a top forum for researchers and practitioners to present and discuss new trends in machine learning, deep learning, pattern recognition and optimization for network architectures and services.
541 _fUABC ;
_cPerpetuidad
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.
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_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:
_z9783031599347
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14525
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-59933-0
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