-- 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).

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.