Quarterly journal published in SPbPU
and edited by prof. Peter Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
IOT DEVICES ANALYSIS USING ENSEMBLE OF NEURAL NETWORKS TRAINED ON UNBALANCED SAMPLE


M. E. Sukhoparov I. S. Lebedev

Annotation: An approach to identifying anomalous situations in network segments of the Internet of Things based on an ensemble of classifiers is considered. Classifying algorithms are tuned for different types of events and anomalies using training samples of different composition. The use of an ensemble of algorithms makes it possible to increase the accuracy of the results due to collective voting. The experiment performed using three neural networks identical in architecture is described. The results of the assessment were obtained both for each classifier separately and with the use of an ensemble.
Keywords: Ensemble of classifiers, anomaly detection, parasitic traffic, information security.
Pages 127-134