Network Traffic Analysis and Control by Application of Machine Learning
Date Issued
2023-07-09
Author(s)
Bogoevski, Zlate
Jovanovski, Ivan
Velichkovska, Bojana
Efnusheva, Danijela
DOI
https://doi.org/10.1007/978-3-031-35314-7_35
Abstract
The purpose of this paper is to analyses how networks work, how data
is transmitted, what information we get from each router during data
transmission, getting to know the basics of machine learning and how to create
models that will learn how networks work. By applying machine learning
methods, results are obtained that show us the shape of a network. With different
methods we can get information about how we can plan the network, in terms of
expanding the network if the capacity of the links is almost full or when one of
the links has predispositions to go from an active state to an inactive one. The
results show satisfactory outcomes through the use of three different machine
learning models that were capable of accurately detecting the functionality of a
port, calculating its utilization and learning when the utilization hits a threshold
of above 75%.
is transmitted, what information we get from each router during data
transmission, getting to know the basics of machine learning and how to create
models that will learn how networks work. By applying machine learning
methods, results are obtained that show us the shape of a network. With different
methods we can get information about how we can plan the network, in terms of
expanding the network if the capacity of the links is almost full or when one of
the links has predispositions to go from an active state to an inactive one. The
results show satisfactory outcomes through the use of three different machine
learning models that were capable of accurately detecting the functionality of a
port, calculating its utilization and learning when the utilization hits a threshold
of above 75%.
Subjects