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Machine Learning for Networking [electronic resource] : 5th International Conference, MLN 2022, Paris, France, November 28-30, 2022, Revised Selected Papers / edited by Éric Renault, Paul Mühlethaler.

Contributor(s): Renault, Éric [editor.] | Mühlethaler, Paul [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 13767Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: X, 180 p. 91 illus., 59 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031361838.Subject(s): Data mining | Computer networks  | Application software | Data Mining and Knowledge Discovery | Computer Communication Networks | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
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. .
In: Springer Nature eBookSummary: 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.
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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. .

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.

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