Machine Learning Empowered Computer Networks
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Machine Learning (ML) transformed various fields through its successful applications across diverse network domains where performance optimization combines with management and security improvements. Our research explains how ML solutions operate across multiple computer networking fields and emphasizes specifically how they enhance load balancing and QoS capabilities and security monitoring. An analysis of prominent ML algorithms coupled with methodology description explains how these systems develop enhanced network capabilities including efficiency with fault resistance and scalability features. This study evaluates both hurdles and forthcoming support for implementing Machine Learning in computer networks by illustrating different learning strategies for handling contemporary network demands. This report investigates current trends in AI-powered network automation along with adaptive traffic management while examining the participation of machine learning techniques in building present and future intelligent networks. management, and security. We provide a detailed mapping of how ML techniques can be utilized in several areas of computer networking but with a special reference to load balancing, QoS, and network security. We delve into these aspects by analyzing prominent ML algorithms and methodologies and how they work towards enhancing the efficiency, fault resistance, and scalability of various network utilities. Moreover, this paper also investigates the challenges as well as future support of incorporating ML in computer networks, elaborating on how different learning approaches (i.e., supervised, unsupervised, and reinforcement) can meet the increased requirements of modern networks. Additionally, it explores emerging trends in AI-powered network automation and adaptive traffic management, providing insights into how machine learning techniques can influence the future of intelligent networks.
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Copyright (c) 2023 Santhosh K, Gary D, Chris C, Samyek N
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