UID:
almahu_9949744365202882
Umfang:
X, 286 p. 116 illus., 90 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2024.
ISBN:
9783031599330
Serie:
Lecture Notes in Computer Science, 14525
Inhalt:
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.
Anmerkung:
-- 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).
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783031599323
Weitere Ausg.:
Printed edition: ISBN 9783031599347
Sprache:
Englisch
DOI:
10.1007/978-3-031-59933-0
URL:
https://doi.org/10.1007/978-3-031-59933-0
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