Analysis of Neural Network Intrusion Detection Methods and Datasets for their Training

Abstract

Approaches based on neural network classifiers to the detection of computer attacks are considered. The problems of training such classifiers are discussed. Data sets on computer attacks for wired and wireless systems are considered. The results of evaluating such sets by the degree of imbalance are given. The problems of learning on unbalanced data sets and approaches to balancing the training set in the case of rare attacks, including those using generative adversarial networks, are described.