Solving the problem of a network device classification based on security parameters using machine learning

Abstract

This article investigates the problem of classifying network devices based on their security parameters using machine learning. Due to the constant growth of threats in cyberspace and the need to ensure a high level of network security, the relevance of using machine learning technologies to identify and classify secure devices is exceptionally high. Therefore, the article considers the specifics of applying machine learning algorithms for classification and regression tasks in network environments. Particular attention is paid to a short review of the algorithms most commonly used for classification tasks. The work describes in detail the process of developing a machine-learning model aimed at classifying network devices according to their security indicators. It considers the selection of appropriate parameters for model training, the process of data preprocessing, and the selection and adjustment of the classification algorithm. The results of model training on actual data are also presented. The process of training and evaluating the accuracy and efficiency of the model is described. The results are analyzed, and the choice of optimal hyperparameters is justified. As a result of the study, an effective machine learning model has been developed that can accurately classify network devices by security level, improving network security when selecting potentially secure devices. The study found that the Random Forest and Decision Tree models showed the highest accuracy in predicting the security state of network devices compared to other models, such as Logistic Regression, k-NN, and Gradient Boosting. The performance of the trained model was tested on a validation dataset. The Decision Tree model correctly predicted the security level of approximately 78% of network devices.