Analysis of the results of application of machine learning аlgorithms in network traffic anomalies detection systems

As of today, the detection of network traffic anomalies is an urgent task of ensuring
the information security of any enterprise. The value of the information that is processed and
stored in the ICS of enterprises increases, thereby increasing the motivation of criminals to
obtain NSD for this information. Therefore, there is a need to implement a solution that will
minimize the risk of attacks on information via the Internet. A modern and effective solution
for an enterprise against threats of this type is the implementation of network traffic
monitoring based on a machine learning algorithm.
There are many machine learning algorithms, so the information security specialist is
faced with the task of deciding on an algorithm that will demonstrate the highest results of
correct response to threats. As part of the study, a comparative analysis of the results of the
application of seven machine learning algorithms in SBA - Naive Bayes, QDA, Random
Forest, Decision Trees, AdaBoost, MLP, KNN was performed.