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

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.
Training and testing of the SBA model was performed on the basis of the CICIDS-
2017 dataset. The use of this sample in the research is due to the presence of records of
modern attacks such as Brute Force, DoS, HeartBleed, WebAttack, Infiltration, Botnet,
DDoS, which makes it more relevant to the current research.