• Arvindhan M

For the first time, machine learning (ML) has seen a rise of use cases across multiple areas in which ML helps solve issues and automate work. Primarily, this is because there has been an increase in the amount of data available, major advances in machine learning algorithms, and more powerful computer hardware. ML has undoubtedly been used to a range of issues in network operation and administration, both ordinary and complicated. Several surveys have been completed for particular networking topics, such as networking technologies, or specialised network technology areas. This study is unique, since it looks at how ML approaches are used in various networking niches across multiple network technologies. Such a discussion aims to benefit readers in a number of ways, from discussing the various learning models and machine learning techniques that are applied to fundamental networking problems, such as traffic prediction, routing, and classification, to dealing with congestion control, resource and fault management, QoS and QoE management, and network security. This survey not only defines the boundaries, but also shines a light on the research and implementation problems and possibilities that will enable ML in networking. Now, because of this, the increasing role of machine learning in networking represents a crucial contribution to the advancement of network operation and administration.

Machine Learning, Networking, Technology

"USE OF MACHINE LEARNING IIN NETWORKING: A DETIALED ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 4, page no.1197-1202, April-2018, Available :http://www.jetir.org/papers/JETIR1804419.pdf

"USE OF MACHINE LEARNING IIN NETWORKING: A DETIALED ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 4, page no. pp1197-1202, April-2018, Available at : http://www.jetir.org/papers/JETIR1804419.pdf