DETECTION OF NETWORK INTRUSIONS USING MACHINE LEARNING ALGORITHMS AND FUZZY LOGIC

The study proposed a model of an intrusion detection system based on machine learning using feature selection in large data sets based on ensemble learning methods. Statistical tests and fuzzy rules were used to select the necessary features. When choosing a basic classifier, the behavior of 8 machine learning algorithms was investigated. The proposed system provided a reduction in intrusion detection time (up to 60%) and a high level of attack detection accuracy. The best classification results for all studied datasets were provided by tree-based classifiers: DesignTreeClassifier, ExtraTreeClassifier, RandomForestClassifier. With the appropriate setting, choosing Stacking or Bagging classifier for model training using all data sets provides a small increase in the classification accuracy, but significantly increases the training time (by more than an order of magnitude, depending on the base classifiers or the number of data subsets). As the number of observations in the training dataset increases, the effect of increasing training time becomes more noticeable. The best indicators in terms of learning speed were provided by the VotingClassifier, built on the basis of algorithms wi