Machine Learning for Networking (eBook, PDF)

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

• Format: PDF

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 180
• Erscheinungstermin: 6. Juli 2023
• Englisch
• ISBN-13: 9783031361838
• Artikelnr.: 68379714

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.