Selected approaches to handling class-imbalanced data in the context of network and network services security

This paper focuses on the problem of network and internet service security using a machine learning approach, in the context of class imbalance. The problem of class imbalance is a crucial one to consider when a knowledge discovery problem is targeted towards nuances in the dataset (untypical internet activities), and a machine learning algorithm trained on an imbalanced dataset is biased towards the majority class rather than to the minority class. In such cases, nuances or incidents such as network attacks may not be detected. In this paper, selected oversampling approaches are validated, and we consider an approach in which redundant oversampling and then undersampling are applied. Finally, using benchmark datasets related to cyber-attack problems, selected machine learning models are applied and conclusions are drawn from the results.