Renault / Boumerdassi / Mühlethaler Machine Learning for Networking

4th International Conference, MLN 2021, Virtual Event, December 1–3, 2021, Proceedings
1. Auflage 2022
ISBN: 978-3-030-98978-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 13175, 161 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This 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.

Zielgruppe

Research

Autoren/Hrsg.

Renault, Éric

Boumerdassi, Selma

Mühlethaler, Paul

Inhaltsverzeichnis

Evaluation 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.