Resumo:
With the increasing dependence on technologies on a daily basis, it is evident the concern
to maintain the infrastructures that support its operation, thus guaranteeing a good
experience for the end user. Thus, denial of service attacks are among the main causes
of anomalies in computer networks, which can cause degradation or even interruption of
services. In this context, the application of new technologies, such as artificial intelligence
or machine learning, becomes increasingly necessary to ensure more agility in detecting
problems, reducing their impacts. Thus, this work presents an analysis between different
methods of classifier supervised machine learning, applied to data collected fromnetwork
equipment, of the switch type, in order to detect anomalies in the network infrastructure
of a higher education institution. The machine learning methods used in this work
were: Decision Tree, Random Forest, Extra Tree, Gradient Boosting, Extreme Gradient
Boosting and Histogram Gradient Boosting. The models generated from these methods
showed promise, being able to achieve results with 99.88% in the Weighted F1 metric
and 99.16% of Balanced Accuracy. Other points, such as training time, prediction time
and save file size, were also taken into account for the classification of the best method.
Given the importance of fault detection tools, this work contributes to the definition of
the best approaches and thus allows the development of new and more efficient tools for
this purpose.
