Machine Learning for Networking

6th International Conference, MLN 2023, Paris, France, November 28¿30, 2023, Revised Selected Papers
Herausgegeben:Renault, Éric; Boumerdassi, Selma; Mühlethaler, Paul

• Broschiertes Buch

Produktbeschreibung

This 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.

Produktdetails

• Lecture Notes in Computer Science 14525
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-59932-3
• 2024
• Seitenzahl: 296
• Erscheinungstermin: 28. Mai 2024
• Englisch
• Abmessung: 235mm x 155mm x 17mm
• Gewicht: 453g
• ISBN-13: 9783031599323
• ISBN-10: 3031599322
• Artikelnr.: 70288715

Inhaltsangabe

.- 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).