| Аннотація | Context. The task of using the ensemble of classifiers to detect DoS attacks in large arrays of network traffic data is solved to withstand attacks on the network.
Objective of this paper is to build an ensemble of classifiers that surpasses single classifiers in terms of accuracy.
Method. To achieve the formulated goal an algorithm, that indicates the probability of belonging to certain classes, which return a vector of classification scores for each point, is proposed. The peculiarity of the proposed approach is that for each point from the dataset, the predicted class label corresponds to the maximum value among all scores obtained by classification methods for a given point. As classifiers, decision trees, k-nearest neighbors algorithm, support vector machines with various kernel functions, and naпve Bayes are considered. A comparative analysis of the proposed approach with single classifiers is considered using the following metrics: accuracy, precision, recall, and F-measure.
Results. The exp |