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 experiments have been performed in R 3.4.1 on the NSL-KDD dataset of network attacks, which was divided into three classes (DoS, normal network behavior and other types of attack). Conclusions. The conducted experiments have confirmed the efficiency of the proposed approach. The most accurate result showed an ensemble of five classifiers. The development of techniques for attacks detection based on an ensemble of classifiers avoids the problems inherent in most approaches since it is capable of detecting both known and new attacks with high accuracy. It can be concluded that the proposed approach for network attacks detection is of practical significance. In order to further study the attacks detection in network traffic, studies will be performed on real Big data sets.