Application of neural networks to detect abnormal traffic in the Internet of Things networks

E.E. Istratova

Abstract


The relevance of solving the problem of choosing machine learning models for detecting anomalies in the Internet of Things network traffic is related to the need to analyze a large number of security events to identify abnormal behavior of smart devices. The purpose of the study was to develop and research software for detecting abnormal traffic in the Internet of Things networks based on artificial neural network mechanisms. The article presents the results of the development of a neural network model and software based on it for determining abnormal traffic in the Internet of Things networks based on a multilayer perceptron. Trained on the UNSW-NB15 dataset, the multilayer perceptron uses 47 input features. At the same time, the accuracy of detecting abnormal traffic was 98.82% with a model training time of 13 ms. Also, as part of the study, a comparison of the developed model with software analogues was performed. The difference in the accuracy of anomaly detection by different models does not exceed 1%, while the model training time is significantly lower for the proposed model, which allows it to be applied in real time.

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References


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