Machine Learning for Networking

5th International Conference, MLN 2022, Paris, France, November 28¿30, 2022, Revised Selected Papers
Herausgegeben:Renault, Éric; Mühlethaler, Paul

• Broschiertes Buch

Produktbeschreibung

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning for Networking, MLN 2022, held in Paris, France, November 28-30, 2022.

The 12 full papers presented in this book were carefully reviewed and selected from 27 submissions.

The papers present novel ideas, results, experiences and work-in-process on all aspects of Machine Learning and Networking.

Produktdetails

• Lecture Notes in Computer Science 13767
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-36182-1
• 1st ed. 2023
• Seitenzahl: 192
• Erscheinungstermin: 7. Juli 2023
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 300g
• ISBN-13: 9783031361821
• ISBN-10: 3031361822
• Artikelnr.: 68073086

Inhaltsangabe

Comparison of AI-based algorithms for low energy communication.- Development of an Intent-Based Network incorporating Machine Learning for service Assurance of E-commerce Online Stores.- Cyber-attack proactive defense using multivariate time series and machine learning with Fuzzy Inference-based Decision System.- iPerfOPS: a Tool for Machine Learning-Based Optimization through Protocol Selection.- GRAPHSEC -- Advancing the Application of AI/ML to Network Security through Graph Neural Networks.- Low Complexity Adaptive ML Approaches for End-to-End Latency Prediction.- TDMA-based MAC protocols designed or optimized using Artificial Intelligence for safety data dissemination in Vehicular ad-hoc network: A Survey.- A Machine Learning Based Approach to Detect Stealthy Cobalt Strike C\&C Activities from Encrypted Network Traffic.- Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents.- Deep Learning Based Camera Switching for Sports Broadcasting.- Phisherman: Phishing Link Scanner.- Leader-Assisted Client Selection for Federated Learning in Iot via the Cooperation of Nearby Devices.