Using an ensemble of neural networks trained on an unbalanced sample to classify the state of Internet of Things devices

Abstract

The paper considers an approach to identifying anomalous situations in network segments of the Internet of Things (IoT) based on an ensemble of classifiers and proposes an ensemble of classifying algorithms for detecting an anomalous situation. Classification objects are represented by multiple parameter tuples. Classifying algorithms are tuned for different types of events and anomalies using training samples of different composition. The use of an ensemble of algorithms allows increasing the accuracy of the results due to collective voting. The experiment performed using three neural networks identical in architecture is described. The variety of classifiers in the ensemble when analyzing the state of IoT devices was formed on the basis of a training sample. The results of the assessment were obtained both for each classifier separately and with the use of the ensemble. Despite the fact that the training sample had an imbalance in relation to classes, the test results using averaging of values by the ensemble of classifiers showed an accuracy value of more than 99 %.